Introduction

The global construction industry is at a pivotal moment, grappling with a series of interwoven challenges, including market saturation, diminishing profitability, and growing environmental concerns (Tang et al. 2022). At the same time, developing nations, especially China, are undergoing rapid urbanization and industrialization, which present both tremendous opportunities for economic growth and daunting sustainability challenges. In China, for instance, the construction sector’s output value soared from 9603.113 billion yuan in 2010 to 29,307.83 billion yuan in 2021, representing a striking 200% increase and contributing 7% to the nation’s GDP. However, this growth has come at a high environmental cost (Li and Ma, 2024; Shi et al. 2023), as the sector accounts for nearly 40% of global energy consumption, with China alone responsible for a staggering 28% of this total. The environmental consequences of such expansive growth are profound, encompassing climate change, biodiversity loss, environmental pollution, and a massive increase in construction waste (Alwan et al. 2017; Cheng et al. 2018; Chuai et al. 2021; Li et al. 2017).

In addition to the environmental impact, the construction industry in China is characterized by significant regional disparities, with pronounced gaps in development between coastal and inland regions, as well as between large metropolitan centers and smaller urban clusters. These imbalances are further complicated by uneven access to resources, technology, and capital, which in turn affects the sector’s overall sustainability. Urban sustainability, as a multifaceted concept, is closely linked to the notion of resilience, which is central to the capacity of cities to adapt and thrive under environmental stress (Wan et al. 2025). Resilience thinking emphasizes the integration of ecological, social, and economic dimensions, which is particularly relevant to understanding how the construction sector can contribute to urban sustainability.

The concept of planetary boundaries, which emphasizes the ecological limits within which humanity can operate sustainably, has been widely discussed in recent research. Richardson et al. (2023) highlight that human activities have already transgressed several of these boundaries, placing immense pressure on sustainable development globally. This is particularly evident in rapidly urbanizing regions, where construction activities contribute significantly to environmental degradation. Such frameworks provide crucial insights into the role of construction in exacerbating or mitigating these challenges, further underscoring the need for more sustainable urban growth in regions like Chengdu-Chongqing and beyond.

In response to these challenges, the Chinese government has introduced a strategic framework known as High-Quality Development (HQD), which emphasizes green growth, innovation, inclusivity, and coordination as core principles for guiding the nation’s economic transformation (Song et al. 2024; Wan et al. 2023). This policy aims to drive sustainable practices in the construction sector while fostering equitable development across different regions. Despite the strategic importance of HQD, research on its application to the construction industry remains fragmented. Most studies focus narrowly on isolated aspects of sustainability, such as energy efficiency or waste reduction, without addressing the broader, systemic challenges that influence the sector’s transition toward sustainable development (Wan et al. 2025; Yan et al. 2024).

Moreover, there is a noticeable gap in understanding how sustainability issues intersect with the socio-economic, cultural, and technological factors shaping urbanization in China. While many urban sustainability assessments have embraced multi-indicator frameworks to capture the complex dynamics of urban systems (Shmelev and Brook, 2021; Shmelev 2019), these frameworks have not been fully integrated into analyses of China’s construction sector. Shmelev’s work (2019) on sustainable cities reimagines urban sustainability through multidimensional assessments, focusing on smart solutions to address urban challenges. However, these frameworks often lack specific applications to rapidly urbanizing regions like Chengdu-Chongqing, where unique socio-economic, infrastructural, and policy contexts shape the development trajectory.

This study aims to fill this gap by applying a novel multidimensional analytical framework that integrates coupling coordination, spatial autocorrelation, and obstacle degree models to analyze sustainable development in the Chengdu-Chongqing urban agglomeration. This approach is unique because it combines traditional methods with spatial and temporal analysis, offering a deeper understanding of the regional dynamics and addressing both macro-level trends and micro-level variations in sustainable development. Specifically, our research aims to:

Identify and analyze the critical factors influencing HQD in the construction sector, including the role of regional disparities, policy implementation, and technological advancements, and how these elements interact in the context of rapid urbanization. II. Evaluate the effectiveness of current policies and propose targeted strategies for fostering more sustainable and equitable development within the construction industry, emphasizing the need for tailored policies based on regional differences.

By providing insights into these issues, this study contributes new perspectives to the broader discourse on sustainable urban development. We combine traditional sustainability frameworks with spatial and temporal analysis to offer a more comprehensive understanding of how urbanization and development patterns in China’s construction industry can be more sustainably managed. The findings are expected to generate valuable lessons not only for China but also for other developing nations facing similar urban challenges, providing a roadmap for more balanced and environmentally responsible growth pathways.

Literature review

The sustainable development of the construction industry has garnered substantial attention from scholars in fields such as environmental economics and urban planning, with a particular focus on regions including Europe, North America, Australia, and China (Lu and Zhang, 2022). These studies underscore the essential role that the construction sector plays in urban sustainability, demonstrating that advancements in sustainability within the industry can significantly enhance overall urban resilience and environmental quality (Xu et al. 2019). Despite this, the practical application of sustainable development practices within the construction sector has been sluggish, particularly in China, where economic expansion often takes precedence over environmental considerations (Huang et al. 2024). However, a comparative analysis of these studies reveals that while developed regions have seen more significant integration of sustainability practices in construction, China faces unique challenges, including the rapid pace of urbanization and the prioritization of economic growth, which complicates the implementation of these practices.

Key themes in the literature include the promotion of circular economy principles (Sudarsan and Gavali, 2024), green development practices (Shurrab et al. 2019), and the reduction of carbon footprints and emissions (Zhou et al. 2022). These studies typically draw upon a diverse array of data sources such as big data, case studies, and statistical analyses (Liu et al. 2021; Onat and Kucukvar, 2020), employing various evaluation models and methodologies to assess the effectiveness of sustainable practices in the construction industry (Qiao, W.Z. et al. 2022). However, many of these studies primarily focus on developed economies and do not sufficiently address the contextual factors influencing sustainable construction practices in China. For instance, the emphasis on technological solutions in Europe and North America (Wang et al. 2024) contrasts with China’s challenges of resource allocation, market regulation, and uneven regional development. The resulting policy recommendations have often focused on optimizing industrial structures, regulating market orders, and reforming top-level systems (Zhang et al. 2015). However, a significant gap remains in research that integrates regional resources and spatial effects, which are critical to understanding the geographical dynamics of sustainable development within the construction sector.

In practical terms, while substantial progress has been made toward enhancing efficiency, reducing consumption, and minimizing environmental impact, challenges persist across various stages of the construction process. Each phase of construction, from material extraction and manufacturing to building operation and demolition, impacts the environment and society in different ways (Carvajal-Arango et al. 2019). Consequently, a primary goal of sustainable construction is to minimize negative externalities by maximizing resource recycling and ensuring building quality (Ghisellini et al. 2021). These challenges, however, are magnified in the Chinese context, where rapid urban expansion leads to greater environmental degradation. To this end, a broad range of evaluation methods, tools, and indicators have been developed and extensively researched. Life Cycle Assessment (LCA) has emerged as a widely used approach to assess the environmental impacts of buildings, including energy consumption, carbon emissions, and the recycling of construction waste (Qiao et al. 2022). Other tools, such as green building assessment systems, focus on specific life cycle stages or components of buildings (Haapio and Viitaniemi, 2008; Shashi et al. 2023), providing targeted insights into areas such as design, spatial structure, and construction practices (Monteiro and Soares, 2022; Xu et al. 2023). Despite their value, these tools often lack the capacity to address the specific regional disparities found in China, where local policies, infrastructure, and socio-economic factors play a substantial role in shaping sustainability outcomes.

Digital technologies have also emerged as powerful enablers of sustainability in the construction sector. Building Information Modeling (BIM) is one of the most promising technological innovations, offering a robust platform for quantifying and optimizing environmental impacts throughout the construction process (Bischof et al. 2022). BIM has demonstrated significant potential in reducing carbon footprints, enhancing green building assessments, and supporting sustainable construction practices (Soust-Verdaguer et al. 2017). The technology has been applied in various domains, including decarbonizing building operations (de Bortoli et al. 2023), conducting green building assessments (Wu et al. 2023), and promoting sustainable practices in material selection and construction techniques (Patel et al. 2023). However, in China, the adoption of BIM is still limited due to high costs, a lack of skilled professionals, and insufficient policy support. This disparity in implementation highlights the importance of integrating local context and policy into technological adoption for more effective sustainability outcomes. Additionally, innovations in building materials such as material substitution and recovery (Desai and Bheemrao, 2022; Bergonzoni et al. 2023) offer significant promise for reducing emissions and encouraging more sustainable practices. Yet, these technologies face market acceptance challenges, particularly in regions like Chengdu and Chongqing, where the construction industry is still heavily reliant on traditional building methods. This presents a significant barrier to large-scale adoption, underscoring the need for coordinated regional policies that support the integration of green technologies across the entire urban agglomeration.

In recent years, the concept of HQD has emerged as a new paradigm for sustainable growth under China’s carbon peak and carbon neutrality targets. This framework represents a tailored adaptation of the sustainable development model, specifically addressing the unique challenges faced by developing nations. Research on HQD in the construction sector has predominantly focused on China, where scholars have developed an optimized five-dimensional framework encompassing “green, opening, sharing, innovation, and coordination” to guide the industry toward more sustainable and equitable practices (Wang et al. 2019). However, while this framework offers a comprehensive overview, it often overlooks critical elements of construction quality and fails to fully integrate the socio-economic and environmental dimensions of sustainability. In response, scholars have expanded the HQD framework to include additional dimensions such as “scale growth” and “growth stability,” reflecting the need to balance expansion with sustainability (Sun et al. 2019). Furthermore, researchers have highlighted the importance of incorporating both economic and social dimensions, arguing that the construction industry must not only focus on environmental sustainability but also fulfill broader social responsibilities (Fu and Huang, 2021). Despite these advancements, the development of a systematic, scientific, and universally applicable evaluation system for HQD in the construction sector remains a significant challenge.

Materials and methods

Study area

The Chengdu-Chongqing urban agglomeration is located in southwestern China. It centers around Chengdu and Chongqing and includes 16 cities. In this paper, we use ArcGIS 10.8 to map the study area, as shown in Fig. 1. This area spans approximately 206,000 square kilometers and is home to over 80 million people. The rapid development of the Chengdu-Chongqing urban agglomeration has significantly boosted the construction industry. From 2010 to 2019, the industry’s total output value grew at an average annual rate of 13.4%. By 2019, the number of construction enterprises had increased to 3,000, providing jobs for nearly 2 million people. The per capita construction area reached 19.77 square meters, and the profit-to-tax ratio in 13 cities exceeded the national average by 6%. As the most economically and culturally advanced region in central and western China, the Chengdu-Chongqing urban agglomeration has become a key growth pole for the western region. However, despite this growth, the internal coordinated development of the construction industry has been largely overlooked.

Fig. 1
Fig. 1
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Study area.

The Chengdu-Chongqing urban agglomeration is a strategic link between China’s eastern and western regions. It has become a focal point for population growth, urbanization, and industrial concentration, playing a crucial role in the nation’s regional development strategy. The “Development Plan of Chengdu-Chongqing Urban Agglomeration,” issued by the Chinese government, envisions the region as a national hub characterized by economic vitality, a high quality of life, and environmental sustainability by 2020. By 2030, the goal is to transform the region into a world-class urban agglomeration, marking a significant leap in its development trajectory. This research investigates the shift from traditional construction practices to HQD within this critical period, offering valuable insights into the region’s leapfrog development. The findings also provide lessons for other urban agglomerations worldwide undergoing similar developmental transitions.

(The source of data is the National Platform for Common GeoSpatial Information Services, Version 2024, map number GS (2024) 0650.)

Theoretical Basis

The Chinese government has introduced the concept of HQD with a stronger emphasis on “human development” compared to traditional sustainable development models (Wan et al. 2023). This approach prioritizes a people-centered perspective, seeking to address the fundamental needs of society. HQD is envisioned as an ideal developmental state, transcending mere economic growth and material wealth. It encompasses a holistic improvement across various dimensions, including social, ecological, economic, environmental, cultural, and technological aspects. This shift from a singular focus on economic growth to a more comprehensive model reflects a broader understanding of development (Guo and Zhang, 2021).

The core principles of HQD—innovation, coordination, greenness, openness, and sharing—form a comprehensive framework for development. Innovation serves as a catalyst, driving progress across sectors, while coordination ensures systemic balance. Greenness promotes sustainable environmental practices, openness fosters necessary international and regional exchanges, and sharing strives to distribute the benefits of development broadly (Liu, 2018). Together, these principles aim to address the diverse and evolving needs of the population, proposing a more sophisticated and inclusive form of sustainable development.

The construction industry plays a pivotal role in the national economy, and the quality of its development significantly impacts other sectors (Yang et al. 2020). However, despite its importance, there is currently no universally accepted definition of HQD within this sector (Wu and Zhang, 2021). Building upon previous discussions, this paper defines HQD in the construction industry as the resolution of internal contradictions during its development, the simultaneous realization of the five key development concepts, and the fostering of coordinated growth that enhances overall benefits. The ultimate goal is to improve societal well-being by shifting from unchecked expansion to optimizing structural scales—transitioning from a quantity-driven to a quality-driven development model.

Given the substantial environmental impacts of construction activities, prioritizing sustainability is crucial. This includes controlling carbon emissions, enhancing green spaces, and safeguarding ecological environments (Sun et al. 2019). Furthermore, the industry needs to strengthen its science, technology, and innovation capabilities, fostering high-quality services through innovation-driven processes and efficiency improvements (Liu, 2018). Embracing openness and promoting coordination in income distribution, profitability, and systemic functionality is also key to ensuring a balanced and sustainable development trajectory. By addressing these multidimensional needs, the construction industry can achieve significant advancements, ultimately benefiting the population across economic, social, technological, and ecological spheres.

Recent studies on sustainable development and HQD offer valuable insights into the construction industry’s evolution. However, these studies often focus on isolated aspects, such as innovation or environmental sustainability, without sufficiently addressing the need for coordinated development across multiple dimensions. Achieving HQD requires not only addressing challenges in individual areas but also ensuring a holistic and integrated approach across various sectors (Yuan et al. 2023). Moreover, there has been a lack of comprehensive exploration into the spatial effects and coordinated growth within the construction industry.

This work seeks to address these gaps by focusing on the Chengdu-Chongqing urban agglomeration. Using the five key development concepts as a foundation, we develop an indicator evaluation system to assess HQD within the construction industry. A coupling coordination degree model is employed to evaluate synergies among these indicators, while a spatial autocorrelation model is used to study spatial effects. Additionally, we introduce an obstacle degree model to identify barriers to HQD. The findings culminate in policy recommendations aimed at enhancing HQD in the region, providing valuable guidance for decision-makers in other developing regions facing similar challenges in construction industry management and urban planning (Fig. 2).

Fig. 2
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Research framework.

Construction of indicator system

In response to current developmental imbalances and the need to enhance growth capabilities, the Chinese government has proposed a set of key development concepts: green, opening, sharing, innovation, and coordination (Guo and Gao, 2020). Building upon these concepts, this study constructs an indicator system to assess the HQD of the construction industry in the Chengdu-Chongqing urban agglomeration using available statistical data. The selection of indicators draws upon established literature and expert consultations to ensure relevance, comprehensiveness, and accuracy. Indicator weights were determined using the entropy weight method—a widely employed technique designed to minimize subjective biases. This weighting approach significantly affects analytical outcomes by emphasizing the relative importance of critical indicators, such as green infrastructure, carbon emissions, and innovation, thereby influencing the identification of obstacles and spatial patterns (Zhang et al. 2023). To further validate the robustness of our results, sensitivity analyses were performed, confirming that variations in indicator weights do not substantially alter the study’s main conclusions.

Each concept is addressed through specific secondary indicators, which provide a comprehensive measure of the construction industry’s performance across multiple dimensions. The “green” concept focuses on the industry’s environmental impact, particularly its pollution levels and ecological footprint (Deng and Wu, 2023). As a major contributor to carbon emissions, the construction industry plays a pivotal role in environmental sustainability. Thus, this study uses carbon emissions as a primary indicator of green development, while also considering the per capita green space in public parks and the green coverage rate of built-up areas to reflect the greening of urban environments (Wan et al., 2025).

For the “openness” dimension, the evolution of China’s construction industry from state-run enterprises to a diversified market following the reforms of the late 20th century offers a compelling context. Over this period, various forms of ownership, including collective and foreign investment, emerged, leading to greater international engagement and the export of construction technologies (Beladi et al. 2020). Therefore, the openness dimension incorporates both internal and external perspectives. Specifically, the study measures openness using two indicators: the ratio of foreign-invested enterprises to total construction enterprises, and the ratio of state-owned enterprises to total construction enterprises. These indicators capture the degree of external and internal openness, respectively, highlighting the international integration and diversification within the industry.

The “sharing” concept reflects the equitable distribution of the construction industry’s benefits across society, focusing on how these benefits are shared with the broader population. In this context, three indicators are selected: per capita tax revenue, the number of employees in the construction industry, and the proportion of state-owned enterprise employees within the industry. Per capita, tax revenue serves as a measure of the widespread benefits that accrue to society from the construction sector, while the number of industry employees and the share of state-owned enterprise employees in the workforce provide insights into the sector’s contribution to social employment and economic distribution (Yan, 2020).

Regarding the “innovation” dimension, innovation is widely recognized as the principal driver of economic development, and it is essential for transforming traditional and outdated production modes within the construction industry. Innovation manifests in several forms, particularly through advancements in personnel, technology, and equipment (Wang and Cheng, 2024). The indicators selected to measure innovation include labor productivity, the rate of power equipment in construction enterprises, and the rate of technical equipment. Labor productivity reflects the efficiency of innovation, while the rates of power and technical equipment highlight the technological sophistication and industrialization of the sector (Wang et al. 2019).

Finally, the “coordination” dimension emphasizes the harmonious development of industry scale, business conditions, and per capita performance. A balanced growth across these factors is essential for achieving HQD (Liu et al. 2023). This dimension is measured through three indicators: the proportion of total output value from state-owned enterprises within the industry, profit-to-tax ratio, and per capita construction area. The per capita construction area is included as it reflects the spatial distribution and intensity of construction activities relative to the population, providing insights into how evenly the construction sector is spread across the region. These indicators provide a comprehensive view of the industry’s structural scale, its financial contribution to government revenue, and the per capita level of construction activity (Yan, 2020).

The proposed evaluation indicator system for the HQD of the construction industry is summarized in Table 1. In this system, carbon emissions are considered a negative indicator, as higher emissions correspond to less favorable green development. All other indicators are positive, reflecting the goal of maximizing beneficial outcomes. The weighting of each indicator was determined using the entropy weight method, a well-established technique for multi-criteria decision-making that minimizes subjective bias by relying on objective data distribution. This method ensures that the weights assigned to each indicator reflect its relative importance, based on the variation in the data across all indicators. The detailed procedure for calculating the weights can be found in the section “Coupling coordination degree model”, where we describe the process step by step.

Table 1 Evaluation index system of HQD of the construction industry.
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Data

This study collected data from 2010 to 2019 for 14 prefecture-level cities in Sichuan Province, as well as Chengdu and Chongqing, the capital cities of Sichuan Province and the municipality directly under the central government, respectively. Due to difficulties in obtaining data for each district and county in Chongqing, the study merged all districts and counties in Chongqing and did not analyze them separately. The original data were sourced from the China Construction Industry Statistical Yearbook, China Energy Statistical Yearbook, Sichuan Statistical Yearbook, Chongqing Statistical Yearbook, and various statistical bulletins from the cities. Missing data were supplemented using linear interpolation or the mean value method.

This study utilizes data up until the end of 2019, prior to the significant global impacts of the COVID-19 pandemic, including travel restrictions and economic disruptions. While the pandemic, beginning in 2020, led to substantial disruptions in labor, supply chains, and production dynamics in the construction industry, the data for 2020 onwards were not available at the time of this study. The unpredictable fluctuations during this period, caused by the pandemic, were deemed not representative of typical industry trends, which might introduce significant noise that could affect the stability and accuracy of the analysis. The focus of this study is on assessing long-term HQD trends in the construction industry, and the pre-pandemic period (2010–2019) was chosen to ensure the reliability and stability of the results, avoiding the confounding effects of the pandemic’s extraordinary circumstances.

Given the constraints of data availability and the unique disruptions caused by the pandemic, the 2010–2019 period was considered more appropriate for addressing the research questions of this study. However, we acknowledge that the pandemic’s long-term impact is a critical issue for future research, and this limitation will be addressed in the Future Study section.

Methods

To comprehensively explore the factors influencing sustainable growth in the construction industry, this study adopts an integrated methodological framework including the coupling coordination degree model, spatial autocorrelation analysis, and obstacle degree model. The choice of these methods was motivated by their suitability to analyze complex spatial-temporal interactions, regional disparities, and multidimensional barriers.

Compared with dynamic panel models, the coupling coordination degree model offers a more intuitive way to measure synergy and balance between multiple interconnected systems, capturing both their temporal evolution and regional differences clearly (Yu et al. 2025). Spatial autocorrelation analysis complements this approach by explicitly accounting for spatial clustering effects—something traditional econometric methods may overlook (Wan et al. 2024). Meanwhile, although machine learning methods, such as random forests or neural networks, can handle complex interactions and predictive analyses effectively, they often require large datasets and sacrifice interpretability in exchange for prediction accuracy. Given our study’s focus on interpretability and policy relevance within a limited and targeted dataset, these more transparent, spatially-sensitive approaches are preferable.

Pre-processing of the indicators

To eliminate the impact of different dimensions of each indicator on the calculation, the min-max normalization method is used to process as follows (Huang et al. 2025):

$${\text{Positive indicators}}:{y}_{\theta {ij}}=\frac{{x}_{\theta {ij}}-min({x}_{\theta {ij}})}{max({x}_{\theta {ij}})-min({x}_{\theta {ij}})}$$
(1)
$${\text{Negative indicators}}:{y}_{\theta {ij}}=\frac{max\left({x}_{\theta {ij}}\right)-{x}_{\theta {ij}}}{max({x}_{\theta {ij}})-min({x}_{\theta {ij}})}$$
(2)

where \({x}_{\theta {ij}}\) denotes the original value of the \({j}_{{th}}\) indicator for the \({i}_{{th}}\) city, year θ. i = 1, 2,…,m. θ = 1, 2,…,k. j = 1, 2, …,n. \(max({x}_{\theta {ij}})\) and \(min({x}_{\theta {ij}})\) respectively represent the maximum and minimum values of the indicator, while \({{\rm{y}}}_{{\rm{\theta }}{\rm{ij}}}\) denotes the normalized value of the \({j}_{{th}}\) indicator for the \({i}_{{th}}\) city, year θ.

Coupling coordination degree model

Coupling is a phenomenon originating from physics that describes the interaction between two or more subsystems (Cai et al. 2021; Fan et al. 2019). The coupling coordination degree (CCD) is used to measure the degree of interaction and coordination level between systems (Li et al. 2012). One of the research contents of this paper is to measure the coordination development level among the five subsystems of HQD in the construction industry, which is consistent with the application of the coupled coordination degree model. The research data meets the data requirements of the model, so the coupled coordination degree model is chosen for measurement.

In this study, we also incorporate information entropy to calculate the weights of the indicators. Information entropy, derived from information theory, measures the uncertainty or the amount of information contained in a system. In the context of this model, entropy helps determine the weight of each indicator by evaluating its variability (Liang et al. 2024). The higher the entropy of an indicator, the lower its weight, since it contributes less to the system’s overall uncertainty. Conversely, indicators with lower entropy are assigned higher weights, as they provide more unique information about the system. This method ensures that the weights reflect the relative importance of each dimension in a more objective manner, minimizing subjective bias.

The use of entropy-based weighting is particularly relevant in policy-making, as it allows for a more transparent and data-driven allocation of resources and attention to various subsystems in the construction industry. By applying this method, policymakers can better understand the relative importance of different development areas (such as green growth, innovation, and coordination) in achieving sustainable development goals. This helps guide targeted interventions and resource allocation, supporting evidence-based decision-making in the construction sector.

The calculation process of the CCD model is as follows (He et al. 2025; Yu et al. 2025; Zheng et al. 2025):

Step 1: The entropy value of each indicator is obtained by using

$${e}_{j}=-\frac{1}{ln({rn})}\mathop{\sum }\limits_{\theta }\mathop{\sum }\limits_{i}{(p}_{\theta {ij}}ln{y}_{\theta {ij}})$$
(3)

Here, \({e}_{j}\) is the information entropy of indicator j. r is the number of years, and n is the total number of indicators. \({p}_{\theta {ij}}\) is the ratio of the indicator value in year θ to the \({j}_{{th}}\) indicator for the \({i}_{{th}}\) city, \({p}_{\theta {ij}}=\frac{{y}_{\theta {ij}}}{{\sum }_{\theta }{\sum }_{i}{y}_{\theta ij}}\). \({y}_{\theta {ij}}\) is the normalized value of the \({j}_{{th}}\) indicator.

Step 2: The weight of each indicator is obtained from the formula:

$${w}_{j}=\frac{{d}_{j}}{{\sum }_{j}{d}_{j}}$$
(4)

\({d}_{j}\) denotes the information utility of indicator j, \({d}_{j}=1-{e}_{j}\). \({e}_{j}\) is the information entropy of indicator j and \({w}_{j}\) represents the weight of indicator j.

Step 3: Calculate the comprehensive score of each subsystem.

$${S}_{1}=\mathop{\sum }\limits_{j=1}^{m}({w}_{j}{y}_{\theta {ij}})$$
(5)
$${S}_{2}=\mathop{\sum }\limits_{j=1}^{n}({w}_{j}{y}_{\theta {ij}})$$
(6)
$${S}_{3}=\mathop{\sum }\limits_{j=1}^{k}({w}_{j}{y}_{\theta {ij}})$$
(7)
$${S}_{4}=\mathop{\sum }\limits_{j=1}^{p}({w}_{j}{y}_{\theta {ij}})$$
(8)
$${S}_{5}=\mathop{\sum }\limits_{j=1}^{q}({w}_{j}{y}_{\theta {ij}})$$
(9)

\({S}_{1}\),\({S}_{2},{S}_{3},{S}_{4}\), and \({S}_{5}\), respectively, represent the comprehensive scores of the five subsystems: green, open, sharing, innovation, and coordination. m, n, k, p, and q represent the number of indicators for each of the five subsystems. \({w}_{j}\) represents the weight of indicator j and \({y}_{\theta {ij}}\) is the normalized value of indicator j.

Step 4: Calculate the coupling degree of the five subsystems using the formula in Step 3:

$$C={\left\{\frac{{S}_{1}{S}_{2}{S}_{3}{S}_{4}{S}_{5}}{{\left[\frac{{(S}_{1}+{S}_{2}+{S}_{3}+{S}_{4}+{S}_{5})}{5}\right]}^{5}}\right\}}^{1/5}$$
(10)

\(C\) is the degree of coupling.

Step 5: Based on the above steps, establish a coupling coordination model for the HQD of the construction industry:

$$T=\alpha {S}_{1}+\beta {S}_{2}+\gamma {S}_{3}+\delta {S}_{4}+\varepsilon {S}_{5}$$
(11)
$$D=\sqrt{C\times T}$$
(12)

Here, T represents the comprehensive evaluation index of the construction industry. Considering that the influence of the five subsystems (green, openness, sharing, innovation, and coordination) on the HQD of the construction industry is considered equally important, this paper sets the parameter for each subsystem’s weight to 0.2. This uniform allocation is based on the assumption that each subsystem contributes equally to the overall high-quality development of the industry. The choice of 0.2 for each parameter ensures a balanced contribution from all dimensions, reflecting a holistic approach to HQD assessment. D represents the coupling coordination degree of the construction industry. Referring to previous studies (Li et al. 2021; Li et al. 2012; Zhang et al. 2021), this paper classifies the coupling coordination degree of HQD of the construction industry into five categories (Table 2).

Table 2 Classification of coupling coordination degree of HQD in the construction industry.
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Spatial autocorrelation analysis

To further reveal the spatial effect of coupling coordination degree in Chengdu-Chongqing urban agglomeration, this study intends to adopt the spatial autocorrelation method, which is a classical method for analyzing the spatial effect (Li et al. 2022). The spatial autocorrelation method is used to reveal whether the same attribute of the research object has potential interdependence in adjacent spatial locations (Guo et al. 2021). In this study, we use the global Moran index in spatial autocorrelation to analyze the spatial correlation of CCD for HQD of the construction industry in the Chengdu-Chongqing urban agglomeration and use the local Moran index to explore the local spatial clustering features of CCD. The formulas are as follows (He et al. 2025; Nie et al. 2023):

$${Moran}\mbox{'}{s\; I}=\frac{n{\sum }_{i=1}^{n}{\sum }_{j=1}^{n}{w}_{{ij}}({x}_{i}-\bar{x})({x}_{j}-\bar{x})}{\left({\sum }_{i=1}^{n}{\sum }_{j=1}^{n}{w}_{ij}\right){{\sum }_{i=1}^{n}({x}_{i}-\bar{x})}^{2}}$$
(13)
$${Local\; Moran}\mbox{'}{sI}=\frac{n({x}_{i}-\bar{x}){\sum }_{j=1}^{m}{w}_{{ij}}({x}_{j}-\bar{x})}{{\sum }_{i=1}^{n}{({x}_{i}-\bar{x})}^{2}}$$
(14)

Here, \(\bar{x}\) represents the average CCD of the study area, which is calculated as \(\bar{x}=\frac{1}{n}{\sum }_{i=1}^{n}{x}_{i}\), where \({x}_{i}\) and \({x}_{j}\) are the CCD values of sample i and j, respectively. n represents the total number of sample areas, m represents the number of regions adjacent to region i, and \({w}_{{ij}}\) is the spatial weight matrix.

Obstacle degree model

The obstacle degree model (ODM) can identify the obstacle impact of individual indicators on the HQD of the construction industry, which helps propose targeted improvement measures to promote the HQD of the construction industry in the Chengdu-Chongqing urban agglomeration. In this study, the ODM is used to analyze and identify the main obstacle factors. The formula is as follows (Lv et al. 2024; Pang et al. 2024):

$${O}_{\theta {ij}}=\frac{{w}_{j}(1-{y}_{\theta {ij}})}{{\sum }_{j=1}^{n}{w}_{j}(1-{y}_{\theta {ij}})}$$
(15)

Here, \({O}_{\theta ij}\) represents the obstacle index of the \({j}_{{th}}\) indicator for the \({i}_{{th}}\) city, year θ. \({y}_{\theta {ij}}\) is the normalized value of indicator j, and \(1-{y}_{\theta {ij}}\) represents the deviation of the indicator. n is the total number of indicators. \({w}_{j}\) is the weight of indicator j.

Results

The level of high-quality development in the construction industry

Figure 3 shows the level of HQD in the construction industry within the Chengdu-Chongqing urban agglomeration from 2010 to 2019. The comprehensive HQD scores for each city range between 0.1 and 0.65. Over time, the results indicate an overall upward trend in HQD, despite fluctuations. Except for Chengdu and Chongqing, the other 14 cities exhibit similar fluctuation patterns. From 2010 to 2015, all cities except Chongqing experienced a fluctuating upward trend in HQD, reaching their first peak in 2015. Between 2015 and 2019, the HQD level in most cities temporarily declined before rising again, reaching a second peak in 2019. During this period, Chengdu showed a fluctuating downward trend, while Chongqing’s HQD rose from 2010 to 2014, peaking in 2014, and then declined from 2014 to 2019.

Fig. 3
Fig. 3
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HQD level of the construction industry in the Chengdu-Chongqing urban agglomeration. (From left to right, 2010-2019).

In terms of growth rate, HQD in the construction industry exhibited a positive trend across all cities. The top three cities with the highest growth rates were Nanchong, Suining, and Luzhou. Chengdu and Chongqing had the highest average HQD levels, with scores of 0.532 and 0.423, respectively. In contrast, Leshan had the lowest average HQD level, with a score of just 0.137. Overall, the HQD level in the Chengdu-Chongqing urban agglomeration showed a fluctuating upward trend from 2010 to 2019, with an average comprehensive score of 0.227.

The spatiotemporal characteristics of coupling coordination

As shown in Figs. 4 and 5, the coupling coordination results indicate steady growth from “Extremely Imbalanced” to “Moderately Imbalanced” in the five subsystems of the construction industry’s HQD in the Chengdu-Chongqing urban agglomeration from 2010 to 2019. Temporally, each city’s coupling coordination shows a fluctuating upward trend, though at different rates, with an overall positive development trend.

Fig. 4
Fig. 4
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The level of coupling coordination of HQD of the construction industry in the Chengdu-Chongqing urban agglomeration.

Fig. 5
Fig. 5
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Spatial distribution pattern of coupling coordination degree of HQD of the construction industry in the Chengdu-Chongqing urban agglomeration.

Figure 4 highlights the following temporal characteristics of coupling coordination in the construction industry’s HQD: (1) The overall level of coupling coordination in the Chengdu-Chongqing urban agglomeration is relatively low. In 2010, only Chengdu and Chongqing had moderate imbalance levels, with scores between 0.2 and 0.4, specifically 0.26 and 0.24, respectively. The remaining cities were in the “Extremely Imbalanced” range, with Ziyang and Suining having the lowest levels at 0.14. By 2019, five cities—Ziyang, Ya’an, Leshan, Neijiang, and Suining—remained extremely imbalanced, with scores between 0.18 and 0.2. The remaining cities moved to the “Moderately Imbalanced” range. It is clear that from 2010 to 2019, no city reached the “Basic Coordination” level, and most cities remained extremely imbalanced for an extended period. (2) Despite the generally low levels, the coupling coordination for HQD showed an overall improvement. While growth rates varied among cities, most saw fluctuating upward trends. Zigong experienced the highest growth, increasing from 0.16 in 2010 to 0.24 in 2019, marking a 50% growth rate. Conversely, Chongqing had the smallest growth, moving from 0.24 in 2010 to 0.25 in 2019, a modest 4.17% increase. Overall, the coupling coordination levels of cities showed varying degrees of growth, and although the overall increase was modest, the development trend remains positive.

Figure 5 illustrates the spatial distribution of coupling coordination levels in the Chengdu-Chongqing urban agglomeration. The results show two key patterns: (1) Two-Core Dominance: The coupling coordination levels in the Chengdu-Chongqing urban agglomeration exhibit a “two-core dominance” phenomenon. From 2010 to 2019, Chengdu and Chongqing consistently had the highest average coupling coordination levels, with scores of 0.298 and 0.255, respectively. These cities ranked first and second among the 16 cities in the CCD model, with this dominance being more pronounced in the early years. Between 2010 and 2013, only Chengdu and Chongqing were in the moderate imbalance stage, while other cities gradually transitioned from the extremely imbalanced stage to moderate imbalance in subsequent years. This indicates an overall imbalance in the coupling coordination relationship across the agglomeration. Aside from Chengdu and Chongqing, the coupling coordination levels in other regions are relatively low, with only minor differences among them. (2) Spatial Distribution Pattern: The coupling coordination levels display a spatial characteristic of “high around and low in the middle.” The moderate imbalance state is concentrated around Chengdu and Chongqing, extending gradually towards the north and south. However, the central region, including cities like Ziyang, Neijiang, and Suining, remains in a state of extreme imbalance. These findings address the second objective of the study by identifying the levels of HQD in the construction industry across different cities. The results reveal how the coupling coordination degree has evolved and space, allows for predictions of future trends. This provides a crucial foundation for policy formulation.

These findings set the stage for understanding the spatial patterns of coupling coordination, which will be explored further in the next section, where we perform a spatial autocorrelation analysis to investigate the geographical consistency of these coupling coordination patterns and identify the degree of spatial dependency.

Spatial autocorrelation analysis of coupling coordination levels for high-quality development of the construction industry

This study used Moran’s I to investigate the presence of spatial autocorrelation in the Coupling Coordination Degree (CCD) of the 16 cities within the Chengdu-Chongqing urban agglomeration. The findings are presented in Table 3 and Fig. 6. Table 3 shows the results of the Moran’s I calculation from 2010 to 2019. All P-values were below 0.05, and Z-scores exceeded 1.96, indicating that the null hypothesis could be rejected. This confirms that the spatial pattern of CCD is not randomly distributed. Moran’s I value consistently increased from 0.180 to 0.329 over the period, suggesting a clear and stable positive correlation in the spatial distribution of CCD. This means that cities with higher coupling coordination levels tend to have neighboring cities with similarly high levels. To ensure the robustness of Moran’s I model, we have conducted additional analysis using different spatial weight matrices, including both contiguity-based and distance-based matrices, for comparative purposes.

Fig. 6
Fig. 6
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LISA agglomeration figures from 2010 to 2019.

Table 3 Moran’s I, p-value and z-value.
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To further explore the specific spatial distribution characteristics of this region, GeoDa was used to construct a LISA cluster map, identifying significant and insignificant spatial patterns. The high–high (H–H) cluster indicates cities with high coupling coordination surrounded by cities with similarly high levels. The low-low (L–L) cluster indicates cities with low coupling coordination surrounded by cities with similarly low levels. The low-high (L–H) cluster represents cities with low coordination surrounded by cities with high coordination, while the high–low (H–L) cluster indicates the opposite. Gray areas represent insignificant clusters.

As illustrated in Fig. 6, the spatial distribution of coupling coordination in the Chengdu-Chongqing urban agglomeration during the past decade primarily shows High–High (H–H) and Low–Low (L–L) clusters, with no instances of Low–High (L–H) or High–Low (H–L) clusters observed. From 2010 to 2019, cities like Ziyang, Ya’an, Leshan, and Yibin consistently formed L–L clusters, indicating low coupling coordination levels in these regions. Suining, which was initially classified as an insignificant area, transitioned to an L–L cluster in 2014 but reverted to an insignificant status in 2015. Chengdu, on the other hand, moved from an insignificant area to an H–H cluster starting in 2012, maintaining this status until 2019. Despite being a major city in the agglomeration, Chongqing remained an insignificant area throughout the decade. Overall, the H–H and L–L clusters emerged as the dominant spatial patterns, exhibiting stability over time. The L–L clusters were predominantly located in the southwest of the urban agglomeration, where CCD values remained relatively low and spatial correlations were strong.

Obstacles to the high-quality development of the construction industry

This study used the obstacle degree model to identify and rank the obstacles to the HQD of the construction industry in the Chengdu-Chongqing urban agglomeration. Figure 7 presents the obstacle degree of the primary indicators (B1-B5), while the ranking of the secondary indicators (C1–C15) is shown in the Appendix. According to Fig. 7, the average obstacle degree order for each primary indicator is B5 > B3 > B4 > B1 > B2. The openness factor (B2) consistently had the smallest impact on HQD, while the coordination factor (B5) had the largest, although its obstacle intensity decreased in 2019. In contrast, the green factor (B1) had a relatively small obstacle degree, but it increased in 2019, becoming the highest obstacle that year. The obstacle degrees of the sharing factor (B3) and the innovation factor (B4) remained relatively stable. Overall, the impact of obstacles on HQD in the construction industry has shown a fluctuating downward trend over time.

Fig. 7
Fig. 7
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The degree of barriers to quality development of the construction industry level 1 indicators.

Appendix 1 indicates that although the rankings of obstacle factors vary among cities and years, most cities in the Chengdu-Chongqing urban agglomeration face similar obstacles. The main obstacle factors include carbon emissions (C1), labor productivity (C9), green coverage rate of built-up areas (C3), the proportion of the total output value of state-owned enterprises to the total output value of the construction industry (C12), and profit-to-tax ratio (C13). Factors like the proportion of state-owned enterprise employees to construction industry employees (C7) and the number of construction industry employees (C8) are only significant in certain cities. Notably, in 2019, carbon emissions (C1) became the top obstacle in all cities except Chengdu, indicating a significant restrictive effect on the HQD of the construction industry across various cities in 2019. These findings address the first objective by providing a clear identification and analysis of the obstacles to HQD in the construction industry. This information offers scientific guidance for policymakers and researchers, enabling them to formulate targeted strategies to promote industrial upgrading, technological advancement, and to overcome bottlenecks in HQD within the construction industry.

Discussion

The Overall Level of High-Quality Development in the Construction Industry is Relatively Low

The results of our study indicate that the overall level of HQD in the construction industry within the Chengdu-Chongqing urban agglomeration remains relatively low. This finding is consistent with previous research by (Wang et al. 2022), which positioned Sichuan and Chongqing within the middle to lower tiers of HQD across Chinese provinces. Within the urban agglomeration, Chengdu and Chongqing exhibit notably higher HQD levels compared to surrounding cities, highlighting a regional development gap.

A key factor contributing to this discrepancy is the “urban suction effect,” where major cities like Chengdu and Chongqing, owing to their large size and economic strength, attract substantial resources—skilled labor, technology, and capital—that foster accelerated development (Liu et al. 2017). Chengdu, as the capital of Sichuan Province, and Chongqing, as a direct-controlled municipality, enjoy administrative and economic advantages that further concentrate resources in these cities. This centralization leads to a “Matthew effect” in the construction industry, whereby cities with initial advantages continue to accumulate more resources, further widening the development gap between them and smaller, less developed cities (Ni et al. 2024).

The concentration of resources in Chengdu and Chongqing creates a pronounced imbalance in HQD within the construction industry. While these cities benefit from advanced infrastructure and significant investments in sustainable construction practices, smaller surrounding cities often struggle to access the necessary resources for similar advancements (Wan et al. 2024). This uneven distribution not only hampers regional development but also complicates efforts to achieve inclusive and balanced growth within the sector. To address these challenges, targeted policy interventions are required. Strategies should focus on fostering regional cooperation and resource-sharing mechanisms, investing in the development of smaller cities, and implementing policies that incentivize sustainable construction practices throughout the region (Moshood et al. 2024). Such measures would promote a more equitable distribution of HQD benefits, allowing peripheral cities to catch up and align with the higher standards established by major urban centers.

The overall development trend of coupling coordination degree: positive but geographically influenced

The findings show that while the overall coupling coordination degree in the construction industry’s HQD within the Chengdu-Chongqing urban agglomeration is low, it exhibits a positive upward trend. This suggests that development is progressing, albeit slowly, and is likely influenced by the national policies that emphasize “innovation-driven growth,” “green development,” and “open markets.” These policies, laid out in the “Twelfth” and “Thirteenth Five-Year Plans,” have helped to enhance coupling coordination across the five key subsystems of HQD—innovation, coordination, openness, greenness, and sharing. Notably, between 2015 and 2019, several cities transitioned from a state of “extremely imbalanced” to “moderately imbalanced,” reflecting alignment with these national development goals.

Our spatial autocorrelation analysis revealed that Chengdu formed a notable High–High cluster, whereas Chongqing did not exhibit the same significant clustering effect. This discrepancy may be driven by differences in economic policies, industrial structures, and infrastructure between the two cities. Chengdu, as the provincial capital of Sichuan Province, benefits significantly from targeted regional development policies, including preferential investment environments, robust infrastructure networks, and substantial technological and innovation resources. Such advantages have facilitated stronger integration and synergy within Chengdu and its neighboring cities, leading to pronounced clustering effects. In contrast, Chongqing’s relatively dispersed industrial layout and more varied economic structure, as well as less integrated regional policy frameworks, may have resulted in lower spatial synergy and weaker clustering characteristics.

We acknowledge that our analysis using Moran’s I indices may not fully capture the potential nonlinear spatial interactions and micro-level dynamics within the region. While the global and local Moran’s I indices are useful for assessing broad spatial trends, they may not reveal complex spatial patterns or account for potential non-linear relationships between the regions. Future studies could benefit from more advanced spatial techniques, such as machine learning methods, to capture these intricate spatial dynamics and non-linear effects. Given the limitations of our current approach and the data available, we opted for this method to ensure comparability and manageability within the scope of this study.

Comparatively, existing studies on other urban agglomerations, such as the Yangtze River Delta, indicate significantly stronger High–High clustering effects driven by integrated policy frameworks, cohesive infrastructure networks, and coordinated industrial structures (Wang et al. 2025). While not directly analyzed in our study, the comparison with the Yangtze River Delta highlights potential reasons for Chengdu-Chongqing’s current spatial disparities and underscores the importance of coordinated policies and infrastructure connectivity for fostering sustainable regional development. Drawing from these comparative insights, future strategies in the Chengdu-Chongqing urban agglomeration could emphasize enhanced administrative cooperation, improved infrastructure integration, and more coherent industrial planning (Li et al. 2024). Efforts to facilitate the diffusion of innovations and best practices from High–High areas like Chengdu to Low-Low areas would contribute substantially to achieving balanced and coordinated development throughout the region.

Key factors influencing high-quality development

The obstacle degree analysis identifies several key factors impeding HQD in the construction industry. Notably, the coordination factor exerts the most significant hindrance, followed by the sharing and innovation factors. The green factor, while important, has a relatively minor hindering effect, and the openness factor shows the least hindrance. The dominant role of the coordination factor in restricting HQD can be explained by the economic disparities among the cities within the urban agglomeration. These disparities have resulted in asynchronous development, manifesting in imbalances in the scale and coordination of construction industry development across the region. Without adequate coordination, cities cannot achieve a unified approach to sustainable practices, innovation, or resource distribution, thereby preventing the region from achieving its full HQD potential.

Technological progress driven by innovation can indeed play a pivotal role in addressing environmental challenges and improving efficiency across the construction sector. However, its integration with regional coordination can be double-edged. On the one hand, technological advancements, such as smart construction technologies and green building techniques, can help improve the sustainability of the sector as a whole. For instance, new methods of prefabrication or sustainable materials could reduce carbon emissions and resource consumption across cities in the region (Jayawardana et al. 2025). On the other hand, if innovation is not regionally coordinated, it can lead to regional imbalances (Li et al. 2024), where more developed cities adopt advanced technologies more quickly, while smaller or economically disadvantaged cities lag in implementation. This exacerbates existing disparities by concentrating innovation benefits in economically stronger areas, further widening the development gap between cities like Chengdu and Chongqing versus smaller cities in the Chengdu-Chongqing urban agglomeration.

The sharing factor further highlights this issue by reflecting a failure to ensure equitable distribution of the benefits arising from technological innovation. While national policies supporting technological advancements in the construction industry have helped reduce regional carbon emissions, the unequal adoption of these technologies across cities can impede the equitable advancement of the entire region. Without coordinated planning and resource sharing, innovation could inadvertently deepen the gap between highly developed areas and the less developed ones, undermining the goals of high-quality development (HQD) and sustainable growth. This challenge necessitates holistic regional strategies that integrate technological innovation with coordination mechanisms to ensure that the benefits of innovation reach all cities in the agglomeration and contribute to balanced, inclusive growth.

The minor hindrance posed by the green factor underscores the progress made by the national carbon peak policy and other green development initiatives. However, carbon emissions remain a persistent issue; by 2019, carbon emissions had become the top obstacle in almost every city, indicating that environmental pressures continue to constrain the region’s HQD potential. This disconnection between policy-driven improvements and on-the-ground outcomes highlights the complexity of advancing environmental sustainability in practice. In developing green infrastructure and low-carbon construction, stakeholders often encounter insufficient regulatory support, limited technical expertise, and substantial funding gaps that hamper project implementation (Tan and Solangi, 2024). For example, a recent industry survey identified weak government supervision as the single most critical barrier to green building adoption, surpassing even financial and technological hurdles (Wu et al. 2019). Moreover, spatial constraints in densely built cities can limit the integration of new green spaces or eco-friendly facilities, and a lack of community engagement or environmental awareness may further impede the success of sustainability initiatives.

Similarly, labor productivity—a seemingly economic indicator—plays a critical role in these sustainability outcomes. A less productive workforce often reflects skill gaps or inefficient practices that hinder the adoption of innovative green technologies on construction sites (Manoharan et al. 2024). Conversely, strategic improvements in labor productivity (for instance, through training in modern sustainable construction techniques or the use of digital tools like BIM) can synergistically enhance environmental performance by reducing waste and improving resource efficiency. This interplay underscores the multidimensional nature of HQD, wherein economic efficiency and environmental sustainability must progress in tandem for long-term success.

Finally, the openness factor exhibits the least hindrance. The relatively low impact of this factor suggests that the construction industry in the region is generally open to domestic and international markets. However, disparities in the degree of openness across cities likely reflect differences in geographical location, industrial capacity, and economic development levels.

Policy recommendations for enhancing high-quality development

To enhance the HQD level of the construction industry in the Chengdu-Chongqing urban agglomeration and promote sustainable development, the following suggestions are proposed:

  1. I.

    Strengthen Macroeconomic Regulation and Regional Coordination: Government initiatives should strategically leverage the economic capacities of Chengdu and Chongqing, positioning them as growth engines to drive surrounding cities’ development. To ensure feasibility, local governments may encounter certain regulatory and administrative barriers, such as rigid jurisdictional divisions and conflicting policy priorities among different municipalities. To mitigate these challenges, establishing formal regional collaboration mechanisms—such as joint administrative committees or regular inter-city forums—could enhance cross-boundary cooperation. Additionally, targeted infrastructure investments funded through central and provincial government budgets can improve regional connectivity, encouraging equitable resource distribution and balanced growth (Wan et al. 2024).

  2. II.

    Implement Tailored Development Policies with Clear Incentive Mechanisms: Given the pronounced disparities among cities within the agglomeration, tailored policies aligned with individual cities’ characteristics, constraints, and comparative advantages are necessary. Financial constraints might pose substantial challenges to implementation, particularly for smaller cities with limited fiscal budgets. Therefore, policy mechanisms such as targeted tax incentives, subsidies, and fiscal transfer payments from higher governmental levels can be introduced to attract private investments and alleviate financial pressures. Public-private partnerships (PPPs) could serve as effective instruments, especially in infrastructure projects and industrial parks, reducing upfront public expenditure and sharing financial risks with private entities (Wang and Li, 2020).

  3. III.

    Facilitate Technological and Industrial Upgrading with Targeted Support: Technological and industrial upgrades in the construction sector are essential to enhancing sustainability and efficiency. However, barriers including high initial investment costs, resistance to technological change, and inadequate technical skills among workers might hinder effective implementation. To overcome these issues, governments can launch dedicated funds and financial incentives, such as low-interest loans and tax deductions for enterprises investing in green and prefabricated construction technologies. Moreover, establishing specialized training programs and educational initiatives in collaboration with industry associations and vocational institutions can address skill gaps and promote the broader adoption of environmentally friendly construction practices (Yang et al. 2020).

Conclusion and further study

This study investigates the High-Quality Development levels in the construction industry within the Chengdu-Chongqing urban agglomeration, focusing on five key dimensions: green development, openness, sharing, innovation, and coordination. The results show a fluctuating upward trend in High-Quality Development levels from 2010 to 2019, with Chengdu and Chongqing consistently holding the highest levels. While the overall coupling and coordination degree remains low, it demonstrates positive growth over time. Most cities have transitioned from “extremely imbalanced” to “moderately imbalanced”, with Chengdu and Chongqing forming a dominant “two-core” system at the center of the agglomeration. Spatially, the coupling and coordination degree follows a “high around, low in the middle” pattern, with Chengdu showing significant coupling, while other cities, such as Chongqing, lag in this respect.

Despite these positive trends, the study highlights key obstacles to HQD, including challenges in coordination, carbon emissions, labor productivity, and green coverage in built-up areas. Notably, the factor of openness has had minimal impact on hindering HQD development. A limitation of this study is the exclusion of certain indicators due to data constraints and statistical discrepancies. These limitations are largely due to challenges in obtaining accurate and comprehensive data across all regions of the Chengdu-Chongqing urban agglomeration. Specifically, some data points for indicators such as construction waste, energy consumption, and biodiversity impacts were unavailable or inconsistent, reducing the comprehensiveness of the evaluation. Future research could address these gaps by incorporating additional indicators, such as research and development investment, and the contribution of scientific and technological advancements, for a more robust assessment of HQD.

While this study focuses on data from 2010 to 2019, we recognize the profound impact of the COVID-19 pandemic on the construction industry, particularly in terms of labor, supply chain disruptions, and changes in production dynamics. Although this study intentionally excluded data from 2020 to 2022 due to its exceptional and unpredictable nature, we acknowledge that these years could introduce important insights into the future trajectory of high-quality development (HQD) in the construction sector.

In future research, we plan to incorporate post-2019 data, which will allow for a more comprehensive assessment of the long-term effects of the pandemic on the construction industry. We intend to compare pre- and post-pandemic trends, providing valuable insights into how the pandemic has altered sustainable construction practices, labor market dynamics, and environmental sustainability efforts. Additionally, future studies could explore the resilience of the construction sector in the face of extreme disruptions, offering a more nuanced understanding of HQD in the context of global challenges such as pandemics, climate change, and economic instability.