Spatial Correlation Network of Construction and Demolition Waste Management Efficiency: A Study Based on an Improved Three-Stage SBM-DEA Model in China

Abstract

Exploring the management efficiency of construction and demolition waste (CDW) and the spatial correlation network across regions in China is essential for promoting sustainable development and optimizing resource allocation. This study utilizes an improved three-stage SBM-DEA model and social network analysis to examine the management efficiency of CDW across 30 regions in China from 2010 to 2020. Research findings indicate that from 2010 to 2020, China’s CDW management efficiency improved, with a clear spatial gradient observed across regions. The eastern regions performed better than the western, northeastern, and central areas. Key factors affecting CDW management efficiency include economic development, infrastructure expansion, government policies, and technological progress. Economic growth was negatively associated with redundancy in labor and machinery, while infrastructure development correlated positively with labor, machinery, and capital redundancy. In some areas, government policies contributed to excessive capital investment, increasing redundancy. Technological progress helped reduce labor and machinery redundancy but had a minimal impact on capital redundancy. The spatial correlation network of CDW management demonstrated a “small-world” structure, maintaining stability in network density, relatedness, and hierarchy, though the network efficiency showed a downward trend. Beijing, Henan, and Xinjiang stood out as key nodes in the network, performing strongly in various centrality measures.

1. Introduction

The rapid growth of construction and demolition waste (CDW) represents a critical environmental challenge in urbanizing regions worldwide. As cities expand globally, including in China, India, Brazil, and Southeast Asia, CDW volume rises, straining waste management systems and contributing to environmental degradation and resource depletion [1]. With accelerating urban expansion, CDW volume, including concrete, metals, and other materials, increases, requiring effective management to mitigate environmental degradation and resource depletion [2]. In China, where construction is closely linked to economic development, CDW presents unique challenges in balancing growth with sustainable waste management [3]. The accumulation of CDW strains local waste treatment facilities, exacerbates pollution, and contributes to land scarcity, highlighting the need for optimized management practices. Due to China’s geographic and economic diversity, the efficiency of CDW management varies greatly across regions [4]. Economically developed areas often feature advanced recycling facilities and policies that support efficient waste handling, while less developed regions face infrastructure limitations, leading to spatial inequalities in CDW management [5]. These disparities are further exacerbated by logistical challenges: transporting CDW across regions is often unfeasible due to high costs and limited infrastructure, especially in economically disadvantaged or geographically isolated areas [6]. This imbalance has created a cycle in which less developed regions suffer from both inadequate waste processing capacity and heightened environmental risks, making a unified approach to CDW management more urgent [7]. Addressing these regional imbalances requires an analysis of CDW management efficiency and spatial network characteristics [8].

Although this study is based on the context of China, it also offers insights for countries worldwide facing similar challenges. Previous studies have mostly focused on isolated metrics or local assessments of waste management, lacking an integrated spatial perspective that considers inter-regional relationships and resource flows. Looking through a spatial network lens can provide insights into the circulation of CDW practices among regions. This study offers a new spatial perspective on CDW management by applying an improved three-stage SBM-DEA model and social network analysis (SNA) to examine the spatial correlation network of CDW management efficiency across 30 regions in China from 2010 to 2020. By employing Stochastic Frontier Analysis (SFA) regression to explore the interactions between economic development, infrastructure development, government policy support, and technological advancement, in relation to the slack values of labor, machinery, and capital inputs. Additionally, the analysis of both individual and overall network characteristics revealed the evolutionary trends of the spatial network of CDW and identified the core nodes within the network. This study provides valuable insights into the infrastructure, economic, and policy factors influencing CDW management efficiency, supporting a more balanced and interconnected approach to sustainable waste management across China.

2. Literature Review

With the rapid expansion of the construction industry, the amount of construction and demolition waste has sharply increased, leading to growing academic focus on its management and resource utilization. Research on construction and demolition waste has primarily focused on several key areas: waste reuse and resource recovery [9], treatment technologies [10], policy and management strategies [11], environmental and economic impact assessments [12], sustainable building design and waste reduction approaches [13], and social and behavioral aspects [14].

Research in waste reuse and resource recovery encompasses various aspects, such as the utilization of recycled aggregates, the production of recycled building materials, and the life cycle assessment (LCA) of construction materials [15,16]. Utilizing recycled aggregates helps to reduce the dependence on natural resources, and the production of recycled building materials promotes a closed-loop system for construction waste, thus minimizing resource consumption and environmental impact [17]. The life cycle assessment (LCA) is employed to assess the environmental impact of recycled materials, thereby offering a scientific basis for developing resource recovery strategies [18]. Research in treatment technologies has focused on waste sorting, crushing, grading, and enhancing waste properties using both physical and chemical methods [19]. In the realm of policy and management, scholars explore how policy incentives and mechanisms can facilitate efficient waste treatment and resource recovery, informed by practical experiences from various regions and countries [20]. Environmental and economic evaluations often involve impact assessments (EIA) and feasibility analyses to assess the environmental and economic benefits of waste management practices [21]. Research on sustainable building design and waste reduction emphasizes minimizing waste generation through the adoption of green building practices and circular economy designs [22]. Social and behavioral research centers on public participation and collaboration mechanisms among various stakeholders [23].

Scholars investigating regional differences highlight the impact of economic development levels [24], disparities in construction industry growth and waste generation [25], policy adaptability [26], and regional variations in technological capacity [27] and infrastructure for waste management [28]. Economic development levels significantly influence waste management capabilities: developed regions benefit from advanced technology and infrastructure, whereas less-developed regions face substantial challenges [29]. Scholars investigate effective resource allocation strategies to mitigate these disparities and ensure the sustainability and feasibility of waste management practices. Variations in construction industry growth lead to differences in the types and quantities of waste generated, with rapidly urbanizing regions producing large amounts of complex waste [30]. Research on policy and regulatory adaptability assesses the effectiveness of existing policies across regions, emphasizing the need for cross-regional coordination in policy to facilitate waste flow and resource recovery [31]. From a spatial perspective, scholars analyze the distribution patterns of waste, the layout of management facilities, and the flow of waste across regions in greater detail. Significant variations exist in the distribution of construction waste between urban, town, and rural areas, and the placement and layout of management facilities have a direct impact on management efficiency [32,33]. Studies also explore the drivers of waste flow, with particular attention paid to the role of policies in shaping flow patterns [34,35]. Analyses of spatial balance and imbalance explore how regional cooperation can address the spatial mismatch between waste generation and processing capacity [36,37].

The existing literature primarily focuses on the reuse of construction and demolition waste, treatment technologies, policy management, and spatial factors influencing waste management. Research on regional disparities in CDW management has mainly concentrated on economic factors, policy adaptability, and facility distribution. However, few studies have explored the interconnections and influences between regions from a network perspective. By constructing a spatially connected network of CDW management efficiency across 30 regions in China from 2010 to 2020, this study uncovers the dynamic evolution patterns of the network and identifies key nodes. Efficiency measurement is crucial in the field of construction and demolition waste management. Traditional DEA models define efficiency through the ratio of inputs to outputs but fail to account for slack variables and environmental factors, which may lead to measurement bias. The improved three-stage SBM-DEA model effectively overcomes these limitations, enhancing the accuracy of efficiency measurement and offering significant practical value. Analyzing construction and demolition waste management from the perspective of spatial correlation networks provides theoretical support for formulating inter-regional collaborative governance policies. The evolution of the spatial correlation network over time reflects the effectiveness of policy implementation, while identifying core nodes provides essential support for cross-regional governance, with more influential nodes shouldering greater responsibility.

3. Research Method and Data

Conventional three-stage DEA methods, based on the CCR or BCC models, often overestimate the efficiency of DMUs when slack inputs or outputs are present, compromising the accuracy of the results. Although the SBM-DEA model introduced by Tone [38,39] addressed slack variables, it could not evaluate multiple efficient DMUs simultaneously. The subsequent SE-SBM model, although an improvement, overlooked external environments and random disturbances, focusing solely on controllable factors. As a result, distinguishing inefficiencies caused by internal management from those influenced by external factors remained challenging. To resolve this, Fried et al. [40] integrated DEA with Stochastic Frontier Analysis (SFA) into a three-stage model, effectively mitigating the impact of external and random factors.

3.1. An Improved Three-Stage SBM-DEA Model

The enhanced three-stage SBM model, proposed by Wang et al. [41], assesses the management efficiency of construction and demolition waste (CDW). By eliminating environmental and random influences, the model reduces biases from dimensional or angular parameters, ensuring accurate and objective efficiency assessments. In addition, a panel data framework is used to explore the key factors influencing waste management efficiency.

Step 1: The SBM-DEA model incorporates unexpected outputs to estimate the initial efficiency and relaxation variables of inputs and outputs for each decision-making unit.

Designed as a non-radial, non-angular framework, the SBM (slacks-based measure) model addresses redundant inputs in radial DEA approaches and enhances efficiency assessments by overcoming their one-sided focus on either inputs or outputs [42]. The calculation process is detailed below:

$$\rho =\underset{\lambda ,s^{-},s^{+}}{\mathit{min}}{\displaystyle \frac{1+\frac{1}{m}{\sum }_{i=1}^m\frac{s_i^{-}}{x_{i0}^t}}{1-\frac{1}{q+h}\left({\sum }_{r=1}^q\frac{s_r^{+}}{y_{r0}^t}+{\sum }_{k=1}^h\frac{s_k^{-}}{z_{k0}^t}\right)}}$$

(1)

$$s.t.\sum _{t=1}^T\sum _{j=1,j\ne 0}^n{x}_{ij}^t{\lambda }_j^t-s_i^{-}\le x_{i0}^t,i=\mathrm{1,2},\cdots ,m;$$

$$\sum _{t=1}^T\sum _{j=1,j\ne 0}^n{y}_{rj}^t{\lambda }_j^t-s_r^{+}\le y_{r0}^t,r=\mathrm{1,2},\cdots ,q;$$

$$\sum _{t=1}^T\sum _{j=1,j\ne 0}^n{z}_{ij}^t{\lambda }_j^t-s_k^{-}\le z_{k0}^t,k=\mathrm{1,2},\cdots ,h;$$

$${\lambda }_j^t\ge 0\left(\forall j\right),s_i^{-}\ge 0\left(\forall i\right),s_r^{+}\ge 0\left(\forall r\right),s_k^{-}\ge 0\left(\forall k\right)$$

The parameter $\rho$ denotes the optimal efficiency under variable returns to scale. In period t, $x_{i0}^t$, $y_{r0}^t$ and $z_{k0}^t$ correspond to the i-th input, r-th expected output, and k-th unexpected output for each decision-making unit. Similarly, $s_i^{-}$, $s_r^{+}$, $s_k^{-}$ represent the slack variables, while ${\lambda }_j^t$ indicates the assigned weight during the same period.

Step 2: This step involves applying the Stochastic Frontier Analysis (SFA) model to isolate and mitigate the influence of environmental factors, inefficiencies, and random noise on the input–output slack variables identified in the first stage.

Unlike traditional DEA models, which attribute all inefficiencies to internal factors, SFA separates these influences, enabling a more accurate adjustment of input–output data [43]. Maximum likelihood estimation is employed to determine the parameters for these adjustments. The corresponding regression equation is presented below:

$$S_{il}=f\left(Z_l;{\beta }_i\right)+v_{il}+u_{il}$$

(2)

The relaxation variable $S_{il}$ captures the investment by the l-th decision-making unit for the i-th item, while $Z_k$ denotes the set of environmental variables influencing input relaxation. The coefficient ${\beta }_i$ reflects the environmental variable’s effect during SFA regression. The model defines $u_{il}$ as inefficiency and $v_{il}$ as random error, both independently contributing to the comprehensive error $v_{il}+u_{il}$. Assuming $u_{il}$~$N^{+}\left(u_{il} , {\sigma }_{\mu }^2\right)$ and $v_{il}$~$N\left(0 , {\sigma }_v^2\right)$, the parameter $\gamma ={\displaystyle \frac{{\sigma }_{\mu }^2}{{\sigma }_{\mu }^2+{\sigma }_v^2}}$ distinguishes the dominance of inefficiency $\left(\gamma =0\right)$ or random error $\left(\gamma =1\right)$. The maximum likelihood estimation is used for parameter identification. Additionally, input variables are adjusted to ensure positivity, adhering to DEA model constraints.

$${\widehat{x}}_{il}=x_{il}+\left[\underset{l}{\max }\left\{Z_l\widehat{{\beta }_i}\right\}-Z_l\widehat{{\beta }_i}\right]+\left[\underset{l}{\max }\left\{{\widehat{v}}_{il}\right\}-{\widehat{v}}_{il}\right]$$

(3)

Step 3: The super-efficiency SBM model is used with adjusted inputs and initial outputs to determine the true efficiency of each DMU, effectively removing the impacts of environmental and random factors. The three-stage DEA model integrates SFA regression to separate these effects, ensuring a more precise and impartial evaluation of DMU performance.

3.2. Modified Gravity Model

This study constructs a spatial correlation network for CDW management efficiency across 30 regions in China, focusing on inter-provincial relationships. CDW management displays strong spatial correlation and spillover effects, with geographic distance playing a key role. Many studies use the gravity model to identify regional connections, following the idea that stronger connections arise from greater “mass” and weaken with increasing distance. To capture this spatial network accurately, this study applies a modified gravity model that links CDW management efficiency with geographic proximity. The specific formula appears in Equation (4).

$$F_{ij}=K_{ij}{\displaystyle \frac{M_i{M}_j}{D_{ij}^{\lambda }}},K_{ij}={\displaystyle \frac{M_i}{M_i+M_j}}$$

(4)

where $F_{ij}$ denotes the strength of CDW management efficiency connections between the 30 regions in China. $K_{ij}$ represents the gravitational coefficient. $M_i$ and $M_j$ refer to the CDW management efficiency of region i and region j, respectively. $D_{ij}^{\lambda }$ indicates the geographic distance between region i and region j. $\lambda$ is the distance decay coefficient, usually set to 2. This framework forms the spatial correlation matrix of CDW management efficiency among the 30 regions. The mean value of each row in the matrix serves as a threshold for binarization. Values above the threshold are assigned 1, indicating a CDW management connection between regions; values below the threshold are assigned 0, indicating no such connection.

3.3. Social Network Analysis

3.3.1. Overall Network Analysis

In social network analysis, overall network metrics measure indicators such as density, correlation, hierarchy, and efficiency from a global perspective, capturing various aspects of the network’s properties. By measuring the overall network metrics over time and analyzing their fluctuations, the evolutionary trends of the spatial correlation network for construction and demolition waste management efficiency can be clarified. This helps to analyze the implementation effects of China’s construction and demolition waste management policies.

Network density measures the ratio of actual connections to all possible connections in a network.

$$D={\displaystyle \frac{2L}{N\left(N-1\right)}}$$

(5)

where D is the network density, L is the number of actual edges in the network, N is the number of nodes in the network.

The network correlation degree assesses the relationship strength between nodes in the network. This can be calculated using similarity coefficients.

$$R_{ij}={\displaystyle \frac{\sum _k{A}_{ik}\times A_{jk}}{\sqrt{\sum _k{A}_{ik}^2}\times \sqrt{\sum _k{A}_{jk}^2}}}$$

(6)

where $R_{ij}$ is the similarity between nodes i and j, $A_{ik}$ and $A_{jk}$ represent connections between nodes i, j, and other nodes k.

Network efficiency indicates the speed of information dissemination in the network, calculated as the average inverse of the shortest path between nodes.

$$E={\displaystyle \frac{1}{N\left(N-1\right)}}\sum _{i\ne j}{\displaystyle \frac{1}{d_{ij}}}$$

(7)

where E is the network efficiency, $d_{ij}$ is the shortest path length between nodes i and j.

Network Clustering Coefficient reflects the degree to which nodes in a network tend to cluster together.

$$C={\displaystyle \frac{1}{N}}\sum _i{\displaystyle \frac{2L_i}{k_i\left(k_i-1\right)}}$$

(8)

where C is the network clustering coefficient, $L_i$ is the actual number of edges between the neighbors of node i, $k_i$ is the degree of node i.

Average path length measures the average shortest path between any two nodes in the network.

$$L={\displaystyle \frac{1}{N\left(N-1\right)}}\sum _{i\ne j}{d}_{ij}$$

(9)

where L is the average path length, $d_{ij}$ is the shortest path length between nodes i and j.

3.3.2. Individual Network Analysis

Individual network metrics focus on each node’s position and importance within the network. Metrics like in-degree, out-degree, and centralities identify key nodes and reveal their influence, connectivity, and role in the network. This analysis helps identify nodes that act as major information hubs or bridges, emphasizing their role in network cohesion and information flow.

In-degree is the number of incoming connections a node receives.

$$d_i^{in}=\sum _j{A}_{ji}$$

(10)

where $d_i^{in}$ is the in-degree of node i, $A_{ji}$ indicates a connection from node j to node i.

Out-degree is the number of outgoing connections a node sends out.

$$d_i^{out}=\sum _j{A}_{ij}$$

(11)

where $d_i^{out}$ is the out-degree of node i, $A_{ij}$ represents a connection from node i to node j.

Degree centrality measures the proportion of connections a node has in relation to the network.

$$C_D\left(i\right)={\displaystyle \frac{d_i}{N-1}}$$

(12)

where $C_D\left(i\right)$ is the degree centrality of node i, $d_i$ is the degree of node i, $N$ is the total number of nodes.

Closeness centrality measures a node’s average distance to all other nodes in the network.

$$C_C\left(i\right)={\displaystyle \frac{N-1}{\sum _j{d}_{ij}}}$$

(13)

where $C_C\left(i\right)$ is the closeness centrality of node i, $d_{ij}$ is the shortest path length from node i to node j.

Betweenness centrality indicates how often a node lies on the shortest paths between other nodes.

$$C_B\left(i\right)=\sum _{s\ne i\ne j}{\displaystyle \frac{{\sigma }_{st}\left(i\right)}{{\sigma }_{st}}}$$

(14)

where $C_B\left(i\right)$ is the betweenness centrality of node i, ${\sigma }_{st}$ is the number of shortest paths between nodes s and t, ${\sigma }_{st}\left(i\right)$ is the number of those paths passing through node i.

3.4. Variable Selection

The DEA model evaluates efficiency by analyzing input–output data and constructing a production frontier, which measures the deviation of each unit from it. Accurate evaluation relies on the careful selection of input–output indicators that are non-negative, logically consistent, and quantitatively reasonable. The selected variables, based on previous studies, are outlined in

Table 1. Variable definition in the model.

Variable Type	Variable
Input variables	Labor input (10,000 people)
	Machinery input (CNY 10,000)
	Capital input (CNY 100 million)
Output variables	Economic output (CNY 100 million)
	Construction waste generation (10,000 tons)
Environment variables	Economic development level (Region GDP/Total GDP, %)
	Infrastructure development level (log10 Fixed asset investment per million people)
	Government policy support (log10 Environmental protection expenditure per million fiscal expenditure)
	Technological development level (log10 Environmental technology investment per million people)

.

3.5. Regional Division and Data Sources

This study examines the management efficiency of CDW in 30 regions of China’s construction sector from 2010 to 2020, excluding Tibet, Hong Kong, Macao, and Taiwan due to data constraints. The analysis utilizes data from the China Statistical Yearbook and divides the country into eastern, northeastern, central, and western regions according to economic divisions.

4. Results and Discussion

4.1. Analysis of the Efficiency Results from the Initial Stage SBM Model

From 2010 to 2020, the efficiency of Construction and Demolition Waste (CDW) management in China gradually improved, although significant regional disparities persisted, as shown in

Table 2. Initial stage efficiency of CDW management.

Region	Province	2010	2011	2012	2013	2014	2015	2016	2017	2018	2019	2020	Mean Value
Eastern	Beijing	0.50	0.59	0.61	0.63	0.69	0.69	0.79	1.00	1.06	0.91	1.04	0.77
	Tianjin	0.46	0.75	0.68	0.87	1.03	0.55	1.01	1.01	0.57	1.01	1.02	0.81
	Hebei	0.18	0.21	0.23	0.26	0.29	0.25	0.30	0.28	0.25	0.40	0.49	0.29
	Shanghai	0.37	0.34	0.39	0.45	0.43	0.47	0.56	0.53	0.75	0.54	1.02	0.53
	Jiangsu	0.22	0.27	0.29	0.44	0.54	0.55	0.59	0.70	0.80	1.03	1.02	0.59
	Zhejiang	0.27	0.35	0.40	0.47	0.56	0.59	0.66	0.76	1.06	0.65	0.73	0.59
	Fujian	0.19	0.24	0.27	0.29	0.32	0.33	0.36	0.45	0.52	0.74	1.00	0.43
	Shandong	0.15	0.19	0.20	0.22	0.24	0.23	0.26	0.30	0.41	0.48	0.55	0.29
	Guangdong	0.19	0.23	0.27	0.27	0.29	0.35	0.39	0.50	0.61	0.78	1.05	0.45
	Hainan	1.24	1.03	1.04	1.06	0.86	1.17	0.91	0.91	0.93	1.07	1.20	1.04
Central	Shanxi	0.30	0.35	0.37	0.41	0.39	0.33	0.40	0.44	0.50	0.72	0.76	0.45
	Anhui	0.17	0.22	0.25	0.28	0.30	0.34	0.35	0.39	0.45	0.51	0.70	0.36
	Jiangxi	0.20	0.26	0.30	0.32	0.38	0.38	0.43	0.49	0.58	0.77	1.02	0.47
	Henan	0.16	0.20	0.22	0.24	0.24	0.25	0.27	0.36	0.37	0.56	0.60	0.32
	Hubei	0.17	0.20	0.22	0.27	0.30	0.31	0.38	0.47	0.58	0.96	1.01	0.44
	Hunan	0.19	0.25	0.28	0.30	0.32	0.31	0.33	0.41	0.46	0.61	0.70	0.38
Western	Inner Mongolia	0.18	0.21	0.22	0.25	0.25	0.20	0.26	0.27	0.27	0.35	0.36	0.26
	Guangxi	0.21	0.26	0.31	0.37	1.31	0.45	0.43	0.57	0.71	0.84	1.04	0.59
	Chongqing	0.21	0.29	0.35	0.37	0.44	0.45	0.55	0.63	0.72	0.86	1.03	0.53
	Sichuan	0.17	0.25	0.30	0.29	0.29	0.35	0.49	0.50	0.62	1.01	1.03	0.48
	Guizhou	0.19	0.21	0.32	0.38	0.41	0.40	0.40	0.52	0.56	0.79	1.01	0.47
	Yunnan	0.16	0.20	0.23	0.20	0.25	0.32	0.38	0.52	0.56	0.82	1.04	0.43
	Shaanxi	0.36	0.31	0.39	0.37	0.39	0.37	0.46	0.56	0.59	0.71	0.79	0.48
	Gansu	0.16	0.17	0.21	0.26	0.26	0.26	0.29	0.29	0.31	0.34	0.32	0.26
	Qinghai	1.03	1.16	1.03	0.60	0.57	0.77	0.76	0.68	0.67	1.00	1.11	0.85
	Ningxia	0.30	0.31	0.37	0.34	0.42	0.37	0.43	0.46	0.59	0.70	1.04	0.49
	Xinjiang	0.25	0.27	0.33	0.39	0.39	0.45	0.50	0.68	0.64	0.77	1.05	0.52
Northeastern	Liaoning	0.20	0.25	0.32	0.38	0.30	0.28	0.27	0.36	0.49	0.35	0.58	0.34
	Jilin	0.29	0.36	0.25	0.31	0.34	0.27	0.39	0.59	0.58	0.51	0.68	0.42
	Heilongjiang	0.31	0.34	0.52	0.57	0.58	0.41	0.51	0.51	0.44	0.48	1.00	0.51

. Although these disparities began to narrow by 2017, with several regions adopting more efficient waste management systems and technologies, by 2020, some areas had reached peak efficiency, though substantial differences remained, particularly in provinces like Xinjiang and Jiangsu. In general, the eastern coastal regions of China demonstrated higher CDW management efficiency, following the pattern of “Eastern > Western > Northeastern > Central”. Beijing, Tianjin, and Hainan led the country with efficiency scores of 0.77, 0.81, and 1.04, respectively, significantly surpassing those of other regions. These regions displayed exceptional performance in CDW management, achieving notable success through the implementation of innovative practices. In contrast, provinces such as Hebei (0.29) and Shandong (0.29) lagged behind, facing challenges such as outdated infrastructure, weak regulatory enforcement, and technological limitations. Provinces such as Jiangsu (0.59), Zhejiang (0.59), and Guangdong (0.45) demonstrated moderate efficiency, with scores ranging from 0.4 to 0.6, indicating stable performance but with the potential for improvement.

Regions are categorized into four efficiency tiers based on their CDW management performance. The first tier includes Beijing, Tianjin, and Hainan, all with efficiency scores above 0.7, representing the highest level of CDW management. These regions have successfully implemented advanced technologies, strong policies, and active public participation, resulting in high efficiency. The second tier includes Shanghai (0.53), Guangdong (0.59), Chongqing (0.53), and Qinghai (0.85). These regions face challenges in optimizing resource allocation and applying advanced technologies. The third tier consists of Zhejiang (0.59), Shanxi (0.45), Guizhou (0.47), and Heilongjiang (0.51). With scores between 0.4 and 0.5, these regions still rely on basic waste processing methods and lack innovation and efficient recycling systems. The fourth tier includes Liaoning (0.34), Gansu (0.26), Henan (0.32), and Fujian (0.33), all with efficiency scores below 0.4. These regions face the greatest challenges in CDW management, characterized by weak infrastructure and basic waste management models. Inner Mongolia has the lowest score of 0.24, indicating significant deficiencies in waste management.

Regional disparities in efficiency are driven by several factors, including economic investment, technological development, and environmental policies. Economically developed regions, such as the eastern coastal areas, can allocate more resources to building modern waste management systems. In contrast, economically underdeveloped regions face limitations in financial resources and outdated technologies. Technological development is crucial in determining CDW management efficiency, with advanced technologies for waste sorting, recycling, and repurposing widely adopted in high-efficiency regions. Other regions still rely on outdated methods, leading to lower recycling rates. Additionally, the strength of environmental policies and regulatory enforcement impacts efficiency. In cities such as Beijing and Tianjin, strict policies and effective enforcement have enhanced CDW management efficiency. In less developed regions, weaker enforcement and less stringent policies result in lower performance.

4.2. Analysis of the Results from the SFA Regression

Slack variables represent the difference between actual and target inputs, reflecting redundant inputs that limit improvements in construction and demolition waste (CDW) management efficiency. Negative correlations with slack variables suggest that certain environmental factors enhance CDW management efficiency, while positive correlations act as barriers to efficiency improvements. To evaluate these effects, Stochastic Frontier Analysis (SFA) regression is used, with slack variables as the dependent variable. Environmental indicators, such as economic development level, infrastructure development level, government policy support, and technological development level, are used as explanatory variables. This methodology offers a deeper understanding of how various factors interact with slack variables and affect overall CDW management efficiency.

Table 3. Analysis of random frontier (SFA) regression results.

	Labor Investment Coefficient		Machinery Investment Coefficient		Capital Investment Coefficient
	Value	t-Test	Value	t-Test	Value	t-Test
Constant value	260.15	5.84 ***	1.08	0.16	1.97	0.17
Economic development level	(331.57)	(4.20) ***	(106.43)	(2.65) **	(140.59)	(0.35)
Infrastructure development level	5524.57	8.91 ***	1201.24	44.70 ***	2070.67	66.78 ***
Government policy support	0.00	2.85 **	0.00	2.54 **	0.01	25.42 ***
Technological development level	(878.28)	(2.83) **	(112.54)	(37.06) ***	(264.60)	(1.53)
${\mathsf{\sigma }}^2$	7118.36	6628.70	3472.41	3472.38	37,030.39	37,030.38
$\mathsf{\gamma }$	0.84	3.09	1.00	68,727.70	1.00	40,045.91
LR unilateral error	0.97		148.16 ***		10.47 ***

Note(s): *** and ** represent passing the significance level tests of 1% and 5%, respectively.

, based on Formula (2), presents the results showing the relationship between slack variables and the previously mentioned environmental indicators.

4.2.1. Economic Development Level

Economic development and construction and demolition waste (CDW) management are closely interconnected, with several influencing factors. Economic growth typically drives increased construction activities, resulting in higher waste generation. This implies a positive correlation between economic development and waste generation. However, economic growth also fosters the adoption of more efficient waste management systems and technologies, enhancing resource utilization and recycling. Table 3 shows a negative correlation between economic development and slack variables for labor and machinery inputs, with coefficients of −331.57 and −106.43, significant at the 1% and 5% levels, respectively. This suggests that higher economic development reduces redundancy in labor and machinery inputs, enhancing overall CDW management efficiency. The insignificant result for capital input suggests that economic development may have a lesser impact on capital redundancy, likely due to other factors. In summary, the results support the expectation that economic growth enhances CDW management efficiency by optimizing labor and machinery usage, while capital input optimization is less influenced by economic development.

4.2.2. Infrastructure Development Level

Infrastructure development has a dual impact on CDW management. On one hand, infrastructure expansion often leads to increased waste generation, putting additional pressure on waste management systems. On the other hand, it offers an opportunity to introduce more advanced waste management technologies and strategies, potentially improving efficiency. However, Table 3 reveals a positive correlation between infrastructure development and redundancy in all three inputs—labor, machinery, and capital. The coefficients for labor (5524.57), machinery (1201.24), and capital (2070.67) are all significantly positive, suggesting that infrastructure expansion may inadvertently result in the overuse of these inputs. This finding contrasts with the common assumption that infrastructure development enhances resource utilization efficiency. One possible explanation is that rapid infrastructure expansion may cause misallocation or inefficient deployment of resources, such as excess labor and machinery, without corresponding improvements in waste management efficiency. Therefore, although infrastructure is crucial for urban development, its growth must be carefully managed to avoid redundant investments in waste management.

4.2.3. Government Policy Support

Government policy is crucial in shaping the efficiency of CDW management. Policy interventions provide essential legal frameworks and financial support for effective waste management practices. However, the effectiveness of these policies depends on their implementation and alignment with actual needs. The regression results in Table 3 show that government policy support has no significant effect on labor or machinery redundancy, but it correlates positively with capital redundancy (0.01, significant at the 1% level). This suggests that government policies may encourage over-investment in capital rather than optimizing its use. This finding contrasts with the expectation that well-designed policies should reduce redundancies across all inputs. One possible reason for this discrepancy is that government policies, in some cases, may not have been tailored to local realities, leading to inefficient capital allocation. Rather than focusing on optimizing capital resources, policies may have led to redundant capital investments in some regions. Therefore, while government support is essential, its design and implementation must be more targeted and aligned with efficient resource allocation to prevent exacerbating redundancies.

4.2.4. Technological Development Level

Technological advancements play a crucial role in optimizing CDW management by enhancing waste sorting, processing, and recycling capabilities, thereby reducing the need for excessive labor and machinery inputs. The regression results in Table 3 indicate that technological development negatively correlates with slack variables for both labor (−878.28) and machinery (−112.54), significant at the 5% and 1% levels, respectively. This suggests that technological progress effectively reduces redundancy in these inputs, thus improving management efficiency. However, the lack of significance for capital input indicates that technological improvements may not directly affect capital redundancy in the same manner. This may be because technological advancements tend to influence operational processes more directly, rather than reducing capital investment needs. Nevertheless, the findings support the notion that technological progress is a key driver of efficiency in CDW management by reducing waste and optimizing resource utilization.

4.3. Analysis of the Efficiency Results from the Third-Stage SBM-DEA Model

The results presented in

Table 4. Efficiency of CDW management in the third stage.

Region	Province	2010	2011	2012	2013	2014	2015	2016	2017	2018	2019	2020	Mean Value
Eastern	Beijing	0.68	0.70	0.73	0.71	0.74	0.73	0.79	0.88	0.90	1.05	1.04	0.81
	Tianjin	1.02	0.72	0.57	0.61	0.61	0.77	1.01	0.79	0.76	1.05	1.03	0.81
	Hebei	0.46	0.45	0.39	0.40	0.41	0.38	0.40	0.40	0.36	0.43	0.41	0.41
	Shanghai	1.01	1.07	0.91	0.80	0.79	1.00	1.01	0.91	1.03	0.71	0.91	0.92
	Jiangsu	0.44	0.37	0.41	0.53	0.63	0.61	0.65	0.75	0.80	1.06	1.02	0.66
	Zhejiang	0.49	0.51	0.53	0.58	0.64	0.67	0.70	0.79	1.07	0.75	0.64	0.67
	Fujian	0.72	0.69	0.57	0.58	0.55	0.52	0.54	0.61	0.68	0.79	1.34	0.69
	Shandong	0.40	0.43	0.36	0.40	0.40	0.38	0.42	0.47	0.56	0.67	0.69	0.47
	Guangdong	0.46	0.48	0.57	0.44	0.44	0.46	0.54	0.59	0.72	1.02	1.03	0.61
	Hainan	1.31	0.76	0.68	0.63	0.65	0.66	0.62	0.61	0.58	0.63	0.58	0.70
Central	Shanxi	0.72	0.75	0.63	0.60	0.52	0.48	0.50	0.54	0.57	0.77	0.66	0.61
	Anhui	0.50	0.54	0.49	0.46	0.45	0.44	0.43	0.43	0.45	0.46	0.49	0.47
	Jiangxi	0.63	0.66	0.59	0.56	0.68	0.49	0.51	0.52	0.53	0.60	0.74	0.59
	Henan	0.42	0.46	0.41	0.41	0.35	0.38	0.38	0.44	0.40	0.64	0.60	0.45
	Hubei	0.44	0.42	0.36	0.39	0.37	0.41	0.40	0.51	0.55	0.85	0.74	0.49
	Hunan	0.50	0.52	0.47	0.46	0.45	0.44	0.44	0.49	0.53	0.63	0.61	0.50
Western	Inner Mongolia	0.64	0.67	0.56	0.60	0.57	0.48	0.49	0.49	0.51	0.56	0.44	0.55
	Guangxi	0.62	0.67	0.57	0.60	0.59	0.60	0.53	0.58	0.66	0.69	0.68	0.62
	Chongqing	0.76	0.85	0.69	0.70	0.69	0.68	0.74	0.86	0.91	1.07	1.02	0.81
	Sichuan	0.50	0.52	0.45	0.42	0.38	0.44	0.51	0.53	0.73	1.03	1.00	0.59
	Guizhou	0.69	0.67	0.62	0.57	0.54	0.48	0.46	0.54	0.55	0.68	0.65	0.59
	Yunnan	0.59	0.63	0.54	0.38	0.45	0.50	0.51	0.70	0.72	1.01	1.03	0.64
	Shaanxi	0.75	0.59	0.57	0.57	0.51	0.49	0.51	0.62	0.70	0.72	0.64	0.61
	Gansu	0.70	0.70	0.61	0.57	0.53	0.50	0.48	0.50	0.49	0.54	0.43	0.55
	Qinghai	1.03	1.13	0.90	0.87	0.81	0.87	0.89	0.84	0.75	1.02	1.03	0.92
	Ningxia	0.73	0.78	0.63	0.63	0.58	0.59	0.56	0.62	0.61	0.62	0.70	0.64
	Xinjiang	0.68	0.65	0.60	0.62	0.54	0.55	0.56	0.66	0.66	0.74	1.01	0.66
Northeastern	Liaoning	0.49	0.50	0.49	0.45	0.42	0.45	0.46	0.55	0.60	0.44	0.54	0.49
	Jilin	0.69	0.71	0.50	0.50	0.47	0.51	0.62	0.88	0.57	0.56	0.83	0.62
	Heilongjiang	0.70	0.68	0.71	0.75	0.67	0.62	0.62	0.64	0.61	0.61	1.00	0.69

provide a clearer picture of the actual efficiency after accounting for environmental variables and random errors. The analysis showed that the CDW management efficiency in 30 regions improved significantly compared to the first stage, as shown in Figure 1. The average efficiency scores for 2020 were 0.68 in the eastern regions, 0.52 in the western regions, 0.65 in the central regions, and 0.60 in the northeastern regions. These findings suggest that, after adjustments, the eastern and western regions reached similar efficiency levels.

The regional trends over time, presented in Figure 1, highlight significant improvements in CDW management efficiency across China. In 2010, the efficiency was initially low, with only Qinghai demonstrating high efficiency (ranging from 0.76 to 1.31). By 2015, regions such as Jiangsu, Zhejiang, and Shanghai had shown significant improvement. By 2020, many regions had advanced into higher efficiency bands (ranging from 0.64 to 1.31), signaling widespread improvements in CDW management. After removing environmental variables and random errors, the analysis provided a clearer view of actual efficiency. The adjustments revealed that regions that initially appeared inefficient due to external factors, such as environmental variables, had made consistent progress over time. The adjustments also further validate the appropriateness of the environmental variables selected in this study.

4.4. Analysis of Overall Network Characteristics

4.4.1. Network Density and Network Correlation Degree

From 2010 to 2020, the spatial network density for CDW management efficiency remained stable, as depicted in Figure 2. This stability reflects consistent interregional connections and resource sharing in CDW management. The network correlation degree consistently remained at 1.0000, signifying spatial interdependence in CDW management efficiency. The regions were highly interconnected in their management practices, with shared standards and mutual learning contributing significantly to overall efficiency. The stability in network density and correlation degree underscores the success of unified standards and interregional learning, facilitating consistent and coordinated efficiency across regions. This collaborative approach aligns with China’s emphasis on circular economy principles and resource utilization, contributing to sustainability and coordinated efficiency across regions. The “Zero-Waste City” initiative and active regional participation have also been pivotal in improving recycling rates, reflecting China’s commitment to sustainable urban development and green growth. The stability in the network over the decade underscores the effectiveness of this collaborative approach in promoting sustainable practices in CDW management, driving green growth and circular economy principles.

4.4.2. Network Hierarchy and Network Efficiency

From 2010 to 2020, the spatial network hierarchy degree for CDW management efficiency remained stable at 0.2407, as shown in Figure 3. Despite the stability in hierarchy, network efficiency decreased from 0.7833 in 2010 to 0.7685 in 2020, with a significant decline observed after 2014. This decline suggests that increasing waste volumes and processing complexity may have outpaced the development of management capacity in certain regions. The decline in network efficiency underscores infrastructure and technical challenges. Economic disparities and imbalances in resource allocation also contribute to varying efficiency levels, with more developed regions being better equipped to manage CDW effectively. The stable network hierarchy degree indicates that unified management efforts have been implemented across regions, reflecting a common framework and alignment in CDW management strategies. However, the decline in efficiency highlights the need for greater support in underdeveloped regions, where challenges in coordination and resource allocation hinder CDW management performance. Addressing regional disparities in economic development and resource distribution is crucial for improving coordination and effectiveness in CDW management. Supporting underdeveloped regions is crucial for ensuring a more balanced and efficient approach to managing CDW, enabling all regions to contribute to and benefit from unified national efforts.

4.4.3. Network Clustering Coefficient and Average Path Length

From 2010 to 2020, the spatial correlation network for CDW management efficiency exhibited high clustering coefficients, ranging from 0.552 to 0.583, suggesting strong local cooperation in management practices within regions, as shown in Figure 4. Additionally, the network showed a short average path length, ranging from 2.364 to 2.503. These characteristics suggest a well-connected and collaborative network that enhances the overall efficiency of CDW management. The high clustering coefficient underscores the strength of local cooperation, while the short average path length signals rapid knowledge exchange and effective communication between regions. This allows regions to rapidly share best practices and solutions, which is crucial for addressing CDW management challenges in a timely manner. The network structure exhibits a small-world configuration, where regions are closely connected through high clustering and efficient information exchange. This small-world structure enables quick responses to emerging issues in CDW management by ensuring the efficient dissemination of knowledge and solutions across regions, playing a vital role in improving management practices and maintaining a sustainable, responsive approach nationwide.

4.5. Analysis of Individual Network Characteristics

Figure 5 presents the spatial correlation network for CDW management efficiency in 2020. Degree centrality, in-degree, out-degree, closeness centrality, and betweenness centrality help clarify each region’s role and status within the network. These centrality indicators offer a clearer understanding of each region’s position and function within the spatial correlation network for construction waste management. Degree centrality reflects a region’s importance and influence within the network. In-degree represents a region’s attractiveness and reputation, positioning it as a model for others to learn from and collaborate with. Out-degree indicates a region’s initiative and sociability, with high out-degree signifying active outreach to establish supportive connections. Closeness centrality reflects a region’s independence and resource access, allowing high closeness regions to connect efficiently and minimize external control. Betweenness centrality reflects a region’s role as a bridge in information and resource exchange, essential for enhancing network collaboration and efficiency.

4.5.1. Degree Centrality

Degree centrality indicates a region’s connectivity and influence within the spatial network, as shown in Figure 6. About 60% of regions have degree centrality above the average, suggesting their crucial role in the network. The five regions with the highest degree centrality are Beijing, Henan, Hainan, Xinjiang, and Tianjin. These regions are central nodes in the network, holding significant control and influence. Beijing, as the political, economic, and cultural center, can significantly influence waste management practices in other regions through policy leadership, technological innovation, and resource distribution. Henan, a central transportation hub, connects multiple provinces and, due to rapid urbanization, provides valuable experience in managing construction and demolition waste. Hainan and Xinjiang, due to their geographical uniqueness, act as key exchange points for various management models and practices, offering valuable examples for neighboring areas.

The regions with the highest in-degree centrality are Xinjiang, Anhui, Henan, Shaanxi, and Shanxi. These regions rely more on external support and influence, making them more receptive to information, technologies, and management practices from other areas. Xinjiang, located in western China and bordering Central Asia, serves as a hub for resource and information exchange. Its high in-degree suggests it can absorb external management techniques to enhance its construction and demolition waste management. Henan and Shaanxi, located in the Central Plains, have historically been key for resource exchange and are more likely to attract support from neighboring regions, especially in policy implementation and technology dissemination. Their high in-degree suggests a strong capacity to interact with and absorb innovations from other areas.

The top five regions based on out-degree centrality are Beijing, Tianjin, Chongqing, Jiangsu, and Anhui. These regions display significant outward influence and can share their construction and demolition waste management experiences and technologies with other areas. As China’s political and cultural center, Beijing leads in waste management innovations and policies, exerting a significant demonstration effect that guides and influences other regions. Tianjin and Jiangsu feature advanced technological innovations and management models, positioning them as key disseminators of best practices. Chongqing and Anhui, with large industrial bases and strong regional coordination, also play a crucial role in providing technical support and facilitating knowledge transfer to other regions.

4.5.2. Closeness Centrality

According to the calculations, 43.33% of regions exhibit above-average closeness centrality, including Jilin, Heilongjiang, Shanghai, Liaoning, and Zhejiang, as shown in Figure 7. These regions demonstrate strong independence in construction and demolition waste (CDW) management, minimizing reliance on external support. These regions enhance waste management efficiency by quickly accessing information and technology, and making self-sufficient management decisions. Shanghai’s high closeness centrality enables rapid updates and optimizations of CDW management systems, with minimal reliance on resources from other regions. Likewise, Jilin and Heilongjiang benefit from efficient internal resource and information flow due to their high closeness centrality. These regions feature autonomous management structures that enable rapid responses to waste management demands, supported by local government initiatives. Liaoning possesses a well-developed waste management system, enables quick access to external information, transforming it into internal policies and technological innovations. Zhejiang, supported by innovative policies and a strong industrial chain, leverages its high closeness centrality to enhance its leadership in CDW management and facilitate the swift implementation of new management models. While high closeness centrality positively impacts management autonomy and efficiency, it may limit cross-regional collaboration and the sharing of technologies. This limitation may hinder the diffusion of successful management practices, thus impacting the overall collaborative effectiveness of the spatially connected CDW management network.

4.5.3. Betweenness Centrality

Betweenness centrality indicates a node’s role as an intermediary in a network, particularly in its ability to facilitate the exchange of information and resources between nodes, as shown in Figure 8. According to the calculations, 43.33% of regions have a betweenness centrality score above the average. The five regions with the highest betweenness centrality are Xinjiang, Henan, Chongqing, Hubei, and Beijing. In the spatial network of construction and demolition waste (CDW) management, regions with high betweenness centrality typically serve as key facilitators of information and resource exchange. These regions function as intermediaries, connecting areas with lower centrality and improving overall waste management efficiency. Although geographically isolated, Xinjiang’s significance lies in its strong transport links, which facilitate the flow of information and resources between neighboring regions. This central role allows Xinjiang to promote collaboration and information exchange in CDW management. Strategically located in central China, Henan serves as a critical link between regions, effectively facilitating the allocation of resources and information across the north, south, east, and west. This makes Henan essential for cross-regional collaboration in waste management. As a major economic and transportation hub in the southwest, Chongqing plays a key role in CDW management by mediating resource distribution and policy coordination. Likewise, Hubei, an economic center in central China and a key part of the Yangtze River Economic Belt, uses its betweenness centrality to bridge regional gaps and promote the sharing of policies and technologies in CDW management. As the capital, Beijing holds significant influence over national policymaking and coordination, making it a key node in the waste management network, especially in governance and strategy development. These regions, with higher betweenness centrality, strengthen the flow of resources and information while enhancing network collaboration through cross-regional waste management coordination and technological advancement.

5. Conclusions and Suggestions

5.1. Conclusions

This study utilizes an improved three-stage SBM-DEA model and social network analysis to examine the management efficiency of construction and demolition waste (CDW) across 30 regions in China from 2010 to 2020. The study reveals both the overall and individual network indicators of the spatial correlation network for CDW management efficiency. The main conclusions are as follows:

From 2010 to 2020, China’s overall CDW management efficiency gradually improved. However, there were significant regional disparities, especially in provinces like Xinjiang and Jiangsu. The spatial distribution of CDW management efficiency showed a clear gradient, with eastern regions outperforming the western, northeastern, and central regions in this order. Beijing, Tianjin, and Hainan showed high efficiency scores of 0.77, 0.81, and 1.04, respectively. In contrast, provinces like Hebei (0.29) and Shandong (0.29) demonstrated lower efficiency, limited by factors such as outdated infrastructure, weak supervision, and technological lag. After adjusting for environmental variables and random errors, regional efficiency disparities were significantly reduced, confirming the suitability of the chosen environmental factors and providing insights into actual efficiency levels under external influences.

The SFA regression identified key factors influencing CDW management efficiency, including economic development, infrastructure construction, government policy support, and technological progress. Economic development negatively correlated with the redundancy of labor and machinery inputs, indicating that economic growth helps reduce resource waste. In contrast, infrastructure development positively correlated with labor, machinery, and capital redundancy, suggesting that the unchecked infrastructure expansion may lead to excessive resource use and waste. Government policies significantly impact CDW management, but in some regions, they may lead to excessive capital investment instead of effective utilization, increasing redundancy. Technological progress had a significant impact, especially in reducing labor and machinery redundancy, but little effect on capital redundancy.

Analysis of the overall network indicators showed that the spatial correlation network of CDW management efficiency remained stable in terms of network density, relatedness, and hierarchical structure, reflecting significant spatial interdependence. This also suggests that China has largely established a unified CDW management framework. The decline in network efficiency reflects the rising volume and complexity of waste management, potentially exceeding the capacity of certain regions, particularly less-developed ones, in coordination and resource distribution. The network followed a “small-world” structure with a high clustering coefficient and short average path lengths, enabling rapid response and problem-solving nationwide, thereby improving management practices.

Analysis of individual network indicators shows that, in 2020, Beijing, Henan, and Xinjiang stood out in several centrality measures in the CDW management efficiency network. Specifically, Beijing ranked highly in degree and betweenness centrality, highlighting its central role in policy leadership, technological innovation, and resource coordination. Henan also excelled in degree and betweenness centrality, serving as a transportation hub that facilitates cross-regional resource flow and information exchange. Xinjiang, with its strong transportation network, ranked highly in betweenness centrality, becoming a key node for regional cooperation and resource sharing. In comparison, Tianjin ranked highly in degree centrality but performed weaker in closeness and betweenness centrality, indicating significant influence in fostering collaboration but a more limited role in independence and information flow.

5.2. Suggestions

Promote Balanced Regional Development: Although CDW management efficiency has improved nationwide, significant regional disparities persist, particularly in provinces like Xinjiang and Jiangsu. To address these gaps, the government should provide targeted support to lagging areas, focusing on infrastructure development and technological advancements, to foster more equitable regional growth.

Optimize Infrastructure Development and Resource Allocation: The study found a positive correlation between infrastructure expansion and resource redundancy, indicating that uncontrolled infrastructure growth can lead to resource waste. Therefore, policies should prioritize careful, well-planned infrastructure development, especially in economically disadvantaged areas. Strengthening planning and regulatory oversight will ensure more efficient use of resources.

Encourage Green Economy and Sustainable Practices: Economic development reduces resource waste by decreasing labor and machinery redundancy. The government should promote a green economy and sustainability, particularly by supporting low-carbon technologies and circular economy models, to further improve resource efficiency and waste reduction.

Enhance Government Policies and Regulatory Mechanisms: Although government policies are crucial for CDW management, some regions have faced inefficient capital investment. To address this, the government should enhance monitoring and evaluation mechanisms to ensure effective implementation, particularly regarding investment efficiency and management outcomes, to achieve policy objectives.

Increase Support for Technological Innovation: Technological advancements have significantly reduced labor and machinery redundancy, though their impact on capital redundancy remains limited. The government should boost support for research and development in waste management technologies, promoting the use of smart and automated systems to improve resource efficiency and reduce waste.

Strengthen Regional Cooperation and Information Sharing: The spatial correlation of CDW management efficiency indicates a robust “small-world” network with efficient information flow and collaboration. The government should foster regional cooperation and information sharing, particularly by creating inter-regional waste management platforms that enable the efficient movement of resources and information.

Enhance Local Government Capacity: Regions such as Beijing, Henan, and Xinjiang excel in CDW management, primarily due to the active involvement of local governments in policy leadership, resource coordination, and technological innovation. To replicate these successes, the government should focus on enhancing local government capacity, particularly in policy execution, resource management, and technological innovation.

Limitations

This study analyzed both the overall and individual indicators of the spatial correlation network for construction and demolition waste management efficiency; however, some limitations persist. Due to data access difficulties, we were unable to acquire relevant data from Tibet, Hong Kong, Macau, and Taiwan, leading to an incomplete construction of the spatial association network for construction and demolition waste management efficiency in China. In addition, because the data were not updated to 2024, we were unable to perform a complete time-series analysis. Analyzing the spatial association network of construction and demolition waste management efficiency from a network perspective is valuable; in the future, we intend to use the TERGM model for further analysis. By analyzing structural effects, attribute effects, and other influences within the network, we aim to further explore the spatial association network of construction and demolition waste management efficiency.