1 Introduction

Since 1978, when China initiated its reform and opening-up policy, the construction industry (CI) has experienced a robust average annual growth rate of 12% in its gross output value. This surge has been driven by the escalating demand for infrastructure and residential buildings, paralleling the country’s swift urbanization pace (CSYC, 2022). Such substantial market demand for CI development in China has been the backbone of its consistent expansion. Serving as a crucial pillar of the national economy, the CI significantly contributes to economic growth, mitigates social employment pressures, improves the quality of life for the populace, and aids in the comprehensive development of a moderately prosperous society (Gan & Lei, 2022). Despite these impressive accomplishments, the CI faces challenges related to quality and efficiency. Specifically, the industry is marked by a broad-based development strategy, low labor productivity, high energy consumption, and considerable emissions. Unplanned development, irrational innovation, and hasty advancements in technology have markedly distorted the environment (Ali et al., 2020). Such issues represent significant hurdles to China's goal of achieving carbon neutrality by 2060 and fail to align with the growing expectations of its citizens for an enhanced quality of life (Anonym, 2022).

The United Nations' Agenda 21 for Sustainable Construction in Developing Countries emphasizes that the sustainable advancement of the construction industry (CI) must harmoniously blend economic, societal, and ecological considerations (Ebolor et al., 2022). In China, the CI occupies a pivotal position within the broader economy. Yet, as a highly polluting and energy-intensive sector, it poses significant challenges to sustainable progress (Cheng et al., 2022). Domestically, the CI and its affiliated sectors are responsible for consuming approximately 40% of total energy (Chen et al., 2023). Globally, it accounts for about 30% of all greenhouse gas emissions, a figure expected to rise significantly (Friedlingstein et al., 2021; Huo et al., 2022). These figures underline the environmental cost of the industry's prosperity, marked by energy and material waste (Abdelzaher et al., 2023b). The construction industry has become a sector that needs to be prioritized for achieving sustainable development. As a major contributor to global greenhouse gas emissions, China plays a crucial role in achieving global climate governance goals and resource conservation (Abdelzaher et al., 2023a; Ali et al., 2021). Only by attaining net-zero carbon emissions worldwide by mid-century can we effectively mitigate climate change and ensure sustainable development for future generations (Bongaarts, 2019).

To promote sustainable development, the Chinese government strategically decided in 2017 to prioritize quality over speed. In 2020, China further outlined the ambitious "double carbon" goal, integrating CO2 emission reduction efforts into its national planning framework. Gradually, the country has established a comprehensive carbon peak, carbon neutral, and "1 + N" policy system, tailored to its unique national context (Wang et al., 2023b). This initiative marked the beginning of China's transition from an era of rapid economic growth to a new phase focused on high-quality development. Characterized by innovation, coordination, green initiatives, openness, and sharing, this development paradigm represents an evolution and expansion of the concept of sustainable development. Serving as a key guiding principle for economic and social progress during the 14th Five-Year Plan period (2021–2025), the high-quality development model stands in stark contrast to the previous growth paradigm, which was capital and resource-intensive and often required post-pollution remediation (Wan et al., 2023). Instead, the new model emphasizes sustainability and efficiency, placing innovation and technology at its heart. It also focuses on meeting human needs and fostering sustainable development (Jiang et al., 2021), ultimately aiming for sustained, stable, coordinated, and inclusive growth across the economic, social, environmental, and humanitarian spheres.

The construction industry plays a key role in China's energy use and carbon emissions (Yu, 2022), a fact that is closely related to the national goals of achieving carbon peaking and carbon neutrality. Promoting high-quality development in the construction industry can improve carbon efficiency and reduce emissions related to energy use and industrial processes. This is crucial for achieving the dual carbon goals and ensuring human well-being. Given the dual constraints of development requirements and the resources-environment nexus in the new era, transforming and upgrading the construction industry is especially urgent.

Based on this, the study seeks to facilitate the industry's shift from a traditional model prioritizing speed and scale, towards one that values quality and efficiency. It commits to a quantitative analysis of the construction industry's high-quality development, aiming for significant strides in establishing a robust development system and mechanisms, and fostering regionally coordinated growth. Building upon this, it aims to uncover the spatiotemporal disparities and evolutionary trends of high-quality development in China's construction industry, and to delve deeper into the heterogeneous effects of factors driving spatiotemporal differentiation in high-quality development across different regions.

The contribution of this research lies in creating an evaluative framework inspired by the latest development paradigms and the goal of carbon neutrality. It unveils the spatiotemporal dynamics and influencing factors of high-quality development within the sector, thus bridging an existing research void. Indeed, our findings offer a theoretical foundation for policymakers and industry leaders to craft low-carbon, sustainable strategies for the construction sector. Additionally, they enhance the empirical research corpus for the construction field and related areas globally.

The structure of this paper is as follows. Section 2 provides a literature review on the theoretical connotation and empirical studies. Section 3 introduces the research methodology and data sources. Section 4 demonstrates the spatial–temporal differences and evolutionary trends of HDCI in China, also explores and analyzes the driving factors. Section 5 summarizes the research results, provides a high-quality development path for construction industry, and points out the future research direction.

2 Literature review

As a more desirable state of progress, the exploration of HDCI merits our utmost attention. Upon reviewing and synthesizing the existing scholarly works, it becomes evident that the prevailing research predominantly revolves around three primary dimensions: the analysis of connotation, the assessment of developmental levels, and the examination of influencing factors or strategic approaches.

2.1 Connotation of high-quality development of construction industry

The report of the 19th CPC National Congress on “Establishing a sound economic system for green, low-carbon and circular development” has pointed out the direction for high-quality development in the new era. In terms of theoretical connotation, Yang et al. (2020), starting from the aspects of economic growth, believe that the HDCI is the process of its industrial structure from extensive development to sustainable development model, and in general can meet the requirements of economic development in China at the present stage; Starting from the main contradictions in society, Gao et al. (2021a) believe that the HDCI is to achieve industrialization and greening through talent team training, technological innovation and industrial structure optimization, so as to better meet the development and change of human needs; Guan and Zhu (2021) proposed that in the development process of the construction industry, it should rely on new technologies and new energy to promote the transformation and upgrading of the industry to meet the needs of social and economic development and environmental sustainable development. Scholars have different definitions of HDCI, but they all take into account the triple bottom line, that is, the balance of economic, social and environmental performance (Kazemi et al., 2023; Selmey & Elamer, 2023).

2.2 Evaluation of the development level of construction industry

Currently, there are two perspectives for quantitatively assessing the development of CI. One is to measure the total factor productivity (Solow, 1957; Trinks et al., 2020) from the perspective of input–output to reflect the development level of the construction industry. Kapelko et al. (2014), Ma et al. (2018) calculated the production efficiency of CI in Spain and Australia respectively through the data enveloping model. Moreno et al. (2016) analyzed the technical efficiency of CI in Spain through stochastic frontier analysis (SFA) models. Additionally, Du et al. (2020), Wang et al. (2021) assessed the carbon emission efficiency and energy efficiency of CI in China, using the non-expected output super-efficiency SBM model and the game cross-efficiency DEA model respectively. Many studies have shown that TFP, as a driver of growth, is important for the development of CI, especially in developing countries such as China (Wang et al., 2020). However, the development of CI involves many aspects of sustainable development such as economy, technological innovation, social service and ecological construction. The single TFP value is not enough to fully epitomize the economic impact of CI elements, nor can it reflect the rich connotation of high-quality development.

Another is to portray the level of development of CI by constructing a multi-dimensional evaluation indicators system. For example, Sun et al. (2019) conducted an early study on the HDCI. They proposed an evaluation model based on dimensions such as scale growth, economic—social benefits, industrial structure and sustainable development, which laid the foundation for subsequent research. With the introduction of new development concept, there is a growing trend of conducting research from dimensions such as innovation, coordination, green, openness, and sharing. For example, Wang and Li (2020), Yang et al. (2020), Wu and Zhang (2021), Gao and Li (2021) built indicators systems to measure and analyze the development level of construction industry in a certain province of China from different dimensions. Most scholars try to explore the relevant indicators of high-quality development from the perspective of empirical research, but on the whole, the indicators are not unified, and the focuses are different. This shows that the academic community still needs to conduct deeper research and discussion on the evaluation system of high-quality development.

2.3 Influencing factors of construction industry development

Technological innovation is driving the Fourth Industrial Revolution (Gomaa et al., 2023; Singh et al., 2022), advanced building technologies have significantly changed the operations of the architecture, engineering and construction industry in US (Blinn & Issa, 2022). Meantime, traditional energy consumption inevitably leads to the release of a large amount of carbon emissions. Optimizing the energy consumption structure has become the key to reducing the environmental impact of the construction industry and promoting the green economy (Destek & Sinha, 2020; Feng et al., 2023), the adoption of cleaner building practices and production innovations can contribute to achieving sustainability goals (Ebolor et al., 2022). In addition, Fan et al. (2021) conducted an empirical analysis of the driving factors of CI in Northeast China, and found that the scientific research investment of local governments and enterprises can promote the structural adjustment of CI. Xiang et al. (2019) employed system dynamics to develop a theoretical model for the initiation of the dynamics mechanism of high-quality development. They identified government management, market norms, industry and enterprise development as the four key driving factors for HDCI. In fact, government support, such as fiscal expenditure, legal sounders, is crucial to the strategic performance of decision makers to introduce low carbon principles and make the built environment more sustainable (Munaro et al., 2020). These studies prove that factors such as government support and technological innovation are important drivers of low-carbon circular economy in the construction industry (Abdelzaher & Awad, 2022), and provide an important reference for a more comprehensive understanding and promotion of quality improvement in the construction industry (Hassan et al., 2023; Wuni, 2022; Zhang et al., 2023a).

Based on the current state of research, existing literature provides valuable insights into high-quality development. However, the connotation and evaluation of high-quality development of the construction industry still need to be further explored. In terms of content, existing research has laid a solid foundation for constructing an evaluation index system for high-quality development in the construction industry. However, as can be seen from the literature, most scholars have the following limitations when studying the high-quality development of the construction industry: (1) Most studies are targeted at a specific province or city, with less comparative analysis at the regional level and between provinces, not considering the spatial dependency and interaction between regions. Ignoring spatial effects may lead to distortions in research results. (2) Most research methods adopt principal component analysis, factor analysis, TOPSIS method, etc., lacking in studies on the identification of the spatial–temporal differentiation, evolutionary trends, and influencing factors of the high-quality development level of the construction industry.

Therefore, this study uses data from 30 provinces in China from 2008 to 2020 to analyze the spatial heterogeneity characteristics of HDCI and its driving factors. The present situation of regional differentiation is revealed and the path of promoting high-quality development of construction industry is proposed. Compared with previous studies, the innovations are as follows: (1) In contrast to previous evaluations focusing on total factor productivity or other indexes, this study assess the comprehensive level of HDCI by integrating six dimensions of scale growth, innovation-driven approaches, openness & coordination, green & energy conservation, comprehensive benefits and livelihood sharing. (2) The study introduces a weight allocation method which combines G1 method, entropy method and coefficient of variation method. The internal statistical law and expert authority value between the index data are considered comprehensively, which can effectively overcome the dependence of single weighting. (3) This study uses exploratory spatial data analysis method and Markov chain model to reveal the spatio-temporal differentiation and evolution characteristics of HDCI, and the spatial heterogeneity of driving factors was explored by GTWR model. The spatial effect and spatiotemporal non-stationarity are fully considered, which makes the research system more rigorous and robust.

3 Data and methods

3.1 Research framework

We propose a framework for studying the spatio-temporal evolution of HDCI and its driving factors (Fig. 1). First, provincial panel data are collected to construct an evaluation indicators system integrating the new development concept, and quantitatively measure the level of HDCI in 30 provinces nationwide from 2008 to 2020 based on the combination of assignments. Second, the spatio-temporal differentiation and evolutionary trend characteristics are analyzed through the exploratory spatial data analysis and Markov chain. Geographically and temporally weighted regression model are used to identify the spatial heterogeneity of driving factors. Finally, the paper puts forward relevant policy suggestions and path to promote the low-carbon and high-quality development of construction industry.

Fig. 1
figure 1

Research framework

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3.2 Evaluation system

3.2.1 The connotation of high-quality development of construction industry

High-quality development is a pivotal decision undertaken by the Chinese government, taking into account both the international and domestic political and economic landscapes. In the context of the construction sector, the "dual carbon" target poses a significant challenge to the industry's growth, thereby heightening the sector's eagerness and commitment to accelerating emission reduction efforts. Unlike developed nations, China's ambition for achieving "double carbon" aims to effectively mitigate emissions while ensuring sustainable socio-economic progress. This endeavor facilitates the transition from a "high-growth" paradigm to a "high-quality" one.

The HDCI is no longer solely gauged by its velocity or aggregate magnitude, nor does it hinge on excessive resource allocation to attain swift expansion on a vast scale. Rather, it underscores the efficiency and efficacy of the output relative to the level of input invested (Yang et al., 2020). Therefore, the concept of HDCI should encompass various aspects such as scale growth, energy conservation and emission reduction, sharing, and sustainability. In this study, HDCI is defined as the pragmatic evolution of the extensive development paradigm. It entails striking a harmonious balance between quality enhancement and quantity expansion through technological progress, efficiency optimization, and dynamic adaptations. Moreover, it strives to fulfill the sustainable development demands of the economy, environment, and resources across the entire spectrum of construction and utilization processes.

3.2.2 Indicators

Drawing upon the development concept of “innovation, coordination, green, openness, and sharing”, we conducted an extensive review of literature published in reputable journals and magazines on platforms such as Web of Science, Science Direct, and CNKI in recent years. Aiming at the theme of “sustainability”, the main indicators and factors of CI are analyzed in word frequency. Furthermore, according to the “14th Five-Year Construction Industry Development Plan” issued by the Ministry of Housing and Urban–Rural Development and the opinions of various regions on promoting HDCI, the principles of availability, scientificity and comparability of indicators selection are followed. With reference to previous literature (Abdelzaher, 2023; Gao et al., 2021b; Zhang et al., 2022), this research constructs an evaluation indicator system for the HDCI, which is based on industrial scale, driven by innovation and open coordination, guided by green energy saving, and targeted at comprehensive benefits and people’s livelihood sharing, as shown in Table 1. The weighting of these indicators is determined using the combined weighting methodology detailed in Sect. 3.4.1.

Table 1 Evaluation indicator system of HDCI
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3.3 Data sources

Based on the reliability and availability of data, we design and select the index system. The construction industry data of 30 provinces (Tibet, Hong Kong, Macao and Taiwan were not included in the study due to the large amount of missing data) from 2008 to 2020 are collected for the study. The indicators data are mainly from the China Construction Industry Statistical Yearbook (2009–2021) and China Yearbook (2009–2021) of the National Bureau of Statistics of China, as well as statistical yearbooks and statistical bulletins of national economic and social development issued by various provinces. For very few missing data, linear interpolation method is used to complete. The data on the boundaries of China’s provincial administrative divisions (version 2022) were taken from the Resource and Environmental Science Data Registration and Publication System (Xu, 2023). According to the standards of the National Bureau of Statistics, the country’s 30 provinces are divided into four regions: eastern, central, western and northeastern regions (Fig. 2).

Fig. 2
figure 2

China’s four major regions and provincial administrative divisions

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3.4 Methods

3.4.1 Combination weighting evaluation

  1. (1)

    G1 method

    The G1 method, also referred to as the sequential relation method, is a subjective weighting method designed to enhance the Analytic Hierarchy Process (AHP) originally proposed by scholar Guo Yajun. This method effectively addresses the challenges associated with ensuring consistency after constructing the judgment matrix in AHP. Additionally, it mitigates the drawbacks of AHP related to extensive calculations and numerous comparison elements (Guo & Wang, 2006). The G1 method stands out as a straightforward approach to weight assignment that obviates the need for consistency testing, making it particularly well-suited for evaluating intricate index factors in complex evaluation scenarios (Ye et al., 2023). Specific steps are as follows:

    1. (1)

      Determine sequential relationships. Through expert opinions and relevant literature research, the importance of \(m\) evaluation indicators is determined and ranked, and the importance is recorded as follows in descending order: j1, j2, …, jm.

    2. (2)

      Quantify the relative importance degree between two adjacent indicators:

      $$r_{k} = \frac{{j_{k - 1} }}{{j_{k} }},k = {2},{ 3}, \ldots ,m$$
      (1)

    In the formula, the assignment and meaning of \(r_{k}\) are as follows: 1.8, indicators \(j_{k - 1}\) is extremely important than \(j_{k}\); 1.6, strongly important; 1.4, obviously important; 1.2, slightly important; 1.0, equally important.

    1. (3)

      Calculate the weight of indicator j:

      $$R_{j} = \left( {1 + \mathop \sum \limits_{k = 2}^{m} \mathop \prod \limits_{j = k}^{m} r_{j} } \right)^{ - 1}$$
      (2)
  1. (2)

    Entropy weight-coefficient of variation method

Entropy is a measure of uncertainty. When the values of evaluation objects differ greatly on an indicator, the entropy value is small, indicating that the effective information provided by the indicator is large, and the weight of the indicator should also be large (Zare Banadkouki, 2023). Entropy weight is an objective weighting method that reflects the relative importance of indicators by calculating the information entropy weight. Coefficient of variation refers to the ratio of mean and standard deviation, which can effectively reflect the difference of each indicator value. In this paper, the average of the weights of the entropy weight method and the coefficient of variation method is set as the objective weight of the indicator. The steps are:

  1. (1)

    Standardized data. In order to eliminate the influence of dimensions and attributes of indicators on the evaluation results, the original data is standardized. The formula is:

    $$L_{ij} = \left\{ {\begin{array}{*{20}l} {\frac{{x_{ij} - min\left( {x_{j} } \right)}}{{max\left( {x_{j} } \right) - min\left( {x_{j} } \right)}} + 0.0001,} \hfill & { positive \;attributes} \hfill \\ {\frac{{max\left( {x_{j} } \right) - x_{ij} }}{{max\left( {x_{j} } \right) - min\left( {x_{j} } \right)}} + 0.0001,} \hfill & {negative\; attributes} \hfill \\ \end{array} } \right.$$
    (3)

where \(L_{ij}\) is the dimensionless value of indicator; \(x_{ij}\) is the original indicator value; \(max\left( {x_{j} } \right)\) and \(min\left( {x_{j} } \right)\) are the maximum and minimum values of the \(j\) TH indicator, respectively and the value of 0 after standardizing is replaced by 0.0001.

  1. (2)

    Calculate entropy weight:

Calculate the proportion of indicators

$$P_{ij} = \frac{{L_{ij} }}{{\mathop \sum \nolimits_{i = 1}^{n} L_{ij} }}$$
(4)

where \(n\) is the number of samples, and 0 ≤ \(P_{ij}\) ≤ 1.

Calculate the entropy of the jth indicator

$$e_{j} = - \frac{1}{\ln n}\mathop \sum \limits_{i = 1}^{n} P_{ij} {\text{ln}}P_{ij} ,e_{j} \ge 0$$
(5)

Define weights:

$$W_{j} = \frac{{1 - e_{j} }}{{\mathop \sum \nolimits_{j = 1}^{m} 1 - e_{j} }}$$
(6)
  1. (3)

    Calculate the weight of the coefficient of variation:

Calculate the coefficient of variation of indicators

$$v_{j} = \frac{{\sigma_{j} }}{{\overline{x}_{j} }}$$
(7)

where \(\sigma_{j}\) and \(\overline{x}_{j}\) are the standard deviation and mean of the jTH indicator, respectively。

Define weights:

$$W_{j} = \frac{{v_{j} }}{{\mathop \sum \nolimits_{j = 1}^{m} v_{j} }}$$
(8)

The average of the weights obtained by entropy weight method and coefficient of variation method is the objective weight of indicator.

  1. (3)

    Combination of subjective and objective

The determination of the comprehensive weight of indicators involves a judicious blend of subjectivity and objectivity. This approach not only takes into account the subjective expertise of professionals regarding the significance of various indicators but also considers the inherent relationships between the original data of these indicators and their internal statistical patterns. By doing so, it effectively mitigates potential biases that may arise from relying solely on either subjective or objective weighting, thereby enhancing the scientific rigor and rationality of the research methodology. Leveraging the principle of minimum discriminative information (Sun et al., 2022) as a foundation, the calculation of optimal comprehensive weights ensures a balanced consideration of both subjective and objective factors, preventing any undue favoritism towards either aspect. The initial construction of the optimal decision model is as follows:

$$\left\{ {\begin{array}{*{20}l} {min\left( F \right) = \mathop \sum \limits_{j = 1}^{m} \left( {F_{j} \ln \frac{{F_{j} }}{{R_{j} }} + F_{j} \ln \frac{{F_{j} }}{{W_{j} }}} \right)} \hfill \\ {s.t.\mathop \sum \limits_{j = 1}^{m} F_{j} = 1,F_{j} \ge 0} \hfill \\ \end{array} } \right.$$
(9)

The model is solved to obtain the comprehensive weight (\(F_{j}^{*}\)), and Eq. (11) is then used to calculate the construction industry quality development index for province \(i\) in year \(t\).

$$F_{j}^{*} = \frac{{\sqrt {R_{j} W_{j} } }}{{\mathop \sum \nolimits_{j = 1}^{m} \sqrt {R_{j} W_{j} } }}$$
(10)
$$D_{it} = \mathop \sum \limits_{j = 1}^{m} L_{ijt} F_{j}^{*}$$
(11)

Here \(R_{j}\) and \(W_{j}\) represent the subjective and objective weights of the indicator, respectively, while \(L_{ijt}\) denotes the standardized value of the data. The comprehensive weights of indicators are shown in Table 1.

3.4.2 Exploratory spatial data analysis

  1. (1)

    Spatial autocorrelation.

Moran's index is an important indicator for observing spatial correlation and heterogeneity (Dong et al., 2023). The global Moran’s index is shown in Eq. (12), \(w_{ij}\) is the spatial weight matrix, \(S^{2}\) is the sample variance, and \(x_{i}\) and \(x_{j}\) are the HDCI values in the neighboring regions. The Moran’sI takes the range of [− 1,1], and the closer to 1 indicates that the spatial positive correlation of the level of high-quality development of the construction industry between the regions is stronger.

$$Moran^{\prime}sI = \frac{{\mathop \sum \nolimits_{i = 1}^{n} \mathop \sum \nolimits_{j = 1}^{n} w_{ij} \left( {x_{i} - \overline{X}} \right)\left( {x_{j} - \overline{X}} \right)}}{{S^{2} \mathop \sum \nolimits_{i = 1}^{n} \mathop \sum \nolimits_{j = 1}^{n} W_{ij} }}$$
(12)

where \(\overline{X} = \frac{1}{n}\mathop \sum \limits_{j = 1}^{n} X_{i}\), \(S^{2} = \frac{1}{n}\mathop \sum \limits_{i = 1}^{n} \left( {x_{i} - \overline{X}} \right)^{2}\).

  1. (2)

    Standard deviation ellipse.

The standard deviation ellipse can be used to show the distribution direction of a geographic element to reveal the overall characteristics of its spatial distribution. The model mainly includes parameters such as the center of gravity coordinates, half-axis length, and azimuth Angle, etc. The specific calculation process can be referred to Li et al. (2023a), and the center of gravity migration distance of the ellipse is calculated according to the following formula (Liu et al., 2021):

$$S_{{\left( {t + 1} \right) - t}} = R \times \sqrt {\left( {Y_{{\left( {t + 1} \right)}} - Y_{t} } \right)^{2} + \left( {X_{{\left( {t + 1} \right)}} - X_{t} } \right)^{2} }$$
(13)

where (\(X_{t}\),\(Y_{t}\)) and (\(X_{{\left( {t + 1} \right)}}\),\(Y_{{\left( {t + 1} \right)}}\)) represent the center of gravity coordinates of the ellipse between years; \(R\) is a constant of 111.11 km.

3.4.3 Markov chain

This study uses Markov chain to reveal the transfer direction and probability of the HDCI. The \(n \times n\) order probability transfer matrix is constructed, and the element \(P_{ij}\) in the matrix represents the probability that the province with the development level of class \(i\) in period \(t\) will jump to class \(j\) in period \(t + 1\). The probability is calculated using the following formula:

$$P_{ij} = \frac{{n_{ij} }}{{n_{i} }}$$
(14)

where \(n_{ij}\) is the number of provinces whose HDCI has shifted from grade \(i\) to grade \(j\) during the study period; \(n_{i}\) indicates the total number of provinces at grade \(i\).

The spatial markov chain introduces the condition of “spatial lag”, and the spatially weighted average of the attribute values in the neighboring areas of the observed provinces is used as the spatial lag value to analyze the trend of state transfer of samples under the effect of spatial factors (Wang et al., 2023c). The formula for calculating the spatial lag value is:

$$Lag_{a} = \mathop \sum \limits_{b = 1}^{n} Y_{b} W_{ab}$$
(15)

where \(Y_{b}\) is the development level of province \(b\), and the spatial weight matrix \(W_{ab}\) is determined by the adjacency principle (1 if two regions are neighboring and 0 if they are not).

3.4.4 Geographically and temporally weighted regression model

The Geographically Weighted Regression model (GWR) focuses on spatial effects at a particular point in time, but its accuracy can be easily affected by limitations in the number of sample data (Wang et al., 2023a). The Geographically and Temporally Weighted Regression model (GTWR) builds upon the GWR model by introducing a temporal dimension and considering both spatial and temporal non-stationarity. This allows for a reduction in parameter estimation biases and model errors, enabling a more comprehensive analysis of complex spatio-temporal data (Fotheringham et al., 2015). The GTWR model is:

$$Y_{i} = \beta_{0} \left( {u_{i} ,v_{i} ,t_{i} } \right) + \mathop \sum \limits_{k = 1}^{m} \beta_{k} \left( {u_{i} ,v_{i} ,t_{i} } \right)X_{ik} + \varepsilon_{i}$$
(16)

where \(Y_{i}\) is the HDCI index in region \(i\), \(X_{ik}\) is the influence factor \(k\), (\(u_{i} ,v_{i} ,t_{i}\)) is the coordinates of the center of the region, \(\beta_{k} \left( {u_{i} ,v_{i} ,t_{i} } \right)\) is the influence coefficient, \(\beta_{0} \left( {u_{i} ,v_{i} ,t_{i} } \right)\) is the constant, and \(\varepsilon_{i}\) is the residual. This study utilizes ArcGIS software, along with the GTWR model plug-in developed by Huang et al. (2010), to analyze the driving factors behind the spatio-temporal evolution of high-quality development in the regional construction industry.

4 Results and discussion

4.1 Trends in the development index of each dimension

The HDCI indices for each dimension across 30 provinces in China from 2008 to 2020 were computed using the combination weighting method. As seen in Fig. 3a, the development indices of all dimensions exhibit an overall upward trajectory. Notably, the green energy saving index stands out as the highest, highlighting the effective implementation of carbon reduction and environmental protection measures in China's construction industry, aligned with the carbon neutrality goal. However, there are notable fluctuations in the innovation-driven and scale growth indices. Furthermore, the scale growth and comprehensive benefit indices for 2019–2020 show a downward trend due to the impact of the epidemic. This underscores the challenges faced by the construction industry, including limited innovation capacity, low efficiency, and inadequate modernization.

Fig. 3
figure 3

Temporal trend of index of each dimension (a) and HDCI in four regions (b)

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The indices of green energy conservation, open coordination, and people’s livelihood sharing have exhibited a consistent upward trajectory. Notably, the most significant increase was observed in people’s livelihood sharing, which surged by over 84.78%. This was followed by open coordination and scale growth, which recorded increases of 44.10% and 35.85%, respectively. These findings underscore that China’s construction industry is firmly aligned with meeting people’s needs, resulting in remarkable improvements in the level of livelihood sharing. As the market mechanism continues to evolve, the importance of open coordination in driving the industry’s growth will only intensify. Therefore, construction enterprises must prioritize investing in innovation resources, focus on modernizing the construction industry chain, and strive to enhance the quality and efficiency of construction production in the coming years.

4.2 Spatial and temporal pattern of HDCI

The HDCI index in China exhibits a prominent upward trend, as illustrated in Fig. 3, rising from 0.262 in 2008 to 0.352 in 2020, with an average annual growth rate of 2.86%. However, there exist pronounced disparities in both the level and growth rate of HDCI across different regions. In 2008, the HDCI index of the four major regions followed the pattern: Eastern > National > Central > Northeast > Western, with only the eastern region exceeding the national average and the western region lagging behind. By 2020, the regional hierarchy had shifted to: Eastern > Central > National > Western > Northeast. Notably, in recent years, the central region has marginally surpassed the national average, while both the western and northeastern regions have persistently maintained an average annual HDCI index that falls below the national mark. In terms of growth rates, the average annual increases in the eastern, central, western, and northeastern regions were 3.34%, 3.35%, 2.74%, and 0.49%, respectively. Evidently, the central and eastern regions have demonstrated robust development momentum, whereas the western region has witnessed a comparatively slower development, and the northeast has displayed slightly weaker growth.

To discern the spatial and temporal patterns of HDCI, 30 provinces were categorized into five distinct levels. Utilizing ArcGIS, we visualized the spatial patterns for the years 2008, 2012, 2016, and 2020, as depicted in Fig. 4. The medium-level region has gradually extended from east to west, with a growing number of provinces attaining higher levels. Simultaneously, inter-regional disparities have become increasingly apparent. In 2008, only Beijing, Jiangsu, and Zhejiang provinces had reached the medium level, while most other provinces lingered at the primary level. Notably, Guizhou exhibited the lowest HDCI with a score of 0.191. By 2012, remarkable progress in CI development was evident in the eastern and central provinces. Jiangsu and Zhejiang had elevated to higher levels, while nine provinces, including Tianjin, Hubei, Shanghai, Guangdong, Shandong, and Liaoning, had transitioned from the primary to the medium level. However, the western region saw minimal changes during this time frame. By 2016, Jiangsu had emerged as the sole province to achieve a relatively high level, while Zhejiang and Beijing maintained their high-level status. Several western provinces, including Sichuan, Shaanxi, Chongqing, and Guangxi, had progressed from the primary to the medium level. Conversely, Liaoning had regressed to the primary level. As of 2020, all provinces, except Qinghai, Gansu, Ningxia, Guizhou, Hainan, and the northeastern provinces, had attained HDCI levels of medium or above. Jiangsu led the pack with the highest HDCI score of 0.587.

Fig. 4
figure 4

Distribution of high-quality development of China’s provincial CI

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It is evident that the HDCI exhibits a spatial pattern characterized by high values in the east and low values in the west. This pattern can be attributed to several reasons, including the steady intensification of industrialization, the progression of information technology, and the expansion of secondary and tertiary industries. The eastern region, favored by advantageous coastal transportation and robust economic growth, possesses a pioneering edge in terms of an advanced and innovative industrial structure (Yang et al., 2023). This allows construction companies to swiftly harness cutting-edge technologies and embrace green production practices, enabling the rational allocation of resources and driving green economic progress (Han et al., 2023). As a result, this region is at the forefront of transitioning to high-quality development. In contrast, the western region faces challenges in its construction industry development due to a scattered population and a relatively underdeveloped industrial foundation (Lang & Ding, 2023). The Northeast, historically an industrial base, has a heavy reliance on traditional industries such as manufacturing and mining. This reliance has resulted in less focus and investment in the modernization and innovation within the construction sector compared to more economically diversified regions (Chen & Zhang, 2021; Zou & Zhang, 2023). This region struggles with significant brain drain, sluggish growth in strategic emerging industries, and difficulties in elevating its HDCI, resulting in a prolonged lag behind the national average.

4.3 Center of gravity migration and discrete trend of HDCI

Between 2008 and 2020, the center of gravity for HDCI in China underwent a noticeable shift. Initially located in Henan Province, its coordinates fluctuated from (112.81° E, 33.53° N) to (112.70° E, 33.15° N) during this timeframe. When referencing Fig. 5, it becomes apparent that from 2008 to 2012, there was a slight tendency for the center of gravity to migrate southeastward, progressing at a rate of 5.01 km per year. However, in 2012, the direction of this shift altered, moving southwestward. From 2012 to 2016, the migration speed was 4.01 km per year, which then accelerated to 4.73 km per year from 2016 to 2020. This indicates that the speed of the center of gravity's movement has been increasing, and interestingly, the displacement in the north–south direction is more significant than that in the east–west direction.

Fig. 5
figure 5

Standard deviation ellipse and gravity center migration

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Changes in Spatial Distribution Pattern: The standard deviation ellipse of HDCI has significantly contracted, decreasing by nearly 300,000 km2, from 3,646,700 km2 in 2008 to 3,386,800 km2 in 2020. This substantial reduction occurred over a twelve-year period. The ellipse has exhibited a shift towards the southeast, followed by a movement towards the southwest, accompanied by an increasing change in azimuth. Both the long and short semi-axes of the ellipse have undergone a decrease, with the long semi-axis reducing from 1255.32 to 1203.75 km and the short semi-axis decreasing from 924.75 to 895.64 km between 2008 and 2020. This trend of diminishing size is particularly evident in the northeast region, leading to a convergence of provinces towards the south, while the northeast gradually becomes more peripheral.

The economic scale of Southwest China may be small, but currently, the region boasts a relatively large-scale industrialization and urbanization infrastructure development (Gao et al., 2020; Shi et al., 2021). Conversely, Northeast China still has a significant proportion of its economy rooted in the secondary industry, leading to regional imbalances in economic growth and ultimately impeding green and low-carbon development efficiency. Provinces like Yunnan and Chongqing, situated in the southwest, stand out with their high forest coverage rates, conferring significant natural advantages. Aligned with the national push for achieving "double carbon" objectives, the integration of these green resources with sustainable industries has been pivotal in driving the high-quality progress of the construction sector (Li et al., 2023b; Xue et al., 2023).

4.4 State transfer trend of HDCI

4.4.1 Global autocorrelation

Table 2 presents the global Moran'sI values for HDCI spanning from 2008 to 2020. The findings reveal that throughout the entire study period, all Moran'sI values are positive, accompanied by a P-value less than 0.01, thus successfully satisfying the significance test at a 99% confidence level. This provides compelling evidence of a robust spatial positive correlation, indicating that the regional HDCI exhibits distinct spatial clustering patterns. More specifically, from 2008 to 2019, the Moran'sI values exhibited a slight upward trajectory, fluctuating within a narrow range of 0.34–0.38. This trend underscores a gradual strengthening of spatial correlation in HDCI and a narrowing of regional disparities. However, by 2020, there was a notable decline in Moran's I. This sudden shift may be attributed to the impact of the COVID-19 pandemic, which appears to have diminished the spatial clustering effect observed previously.

Table 2 Global Moran’sI of HDCI
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4.4.2 The state transition characteristics of HDCI

The index was categorized into four grades: low (L), medium low (ML), medium high (MH), and high (H), based on quartile. A Markov state transition probability matrix (Table 3) was constructed to thoroughly analyze the evolving pattern of HDCI. Disregarding spatial lag, the evolution exhibits the following notable characteristics: Firstly, there is a propensity towards ascending to a superior grade. Specifically, provinces initially classified as low, medium–low, and high grades possess probabilities of 0.274, 0.269, and 0.167, respectively, for ascending to higher grades. Secondly, achieving a leap-forward transfer is challenging. The HDCI demonstrates a gradual progression, with zero probability of a direct leap from a low level to a medium–high or high grade. Lastly, the diagonal value signifies the likelihood of the grade remaining unchanged, and it exceeds the non-diagonal value, indicating that CI maintains stability in its original state throughout the high-quality development process.

Table 3 Transition probability matrix
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The Moran index highlights a substantial spatial correlation in the HDCI. By employing the spatial Markov chain approach, we delve deeper into the characteristics of HDCI grade transfers, accounting for spatial relationships. A comparison of data in Table 3 reveals the following insights:

Firstly, spatial spillover occurs during the high-quality development process of CI. The average peripheral level exerts an influence on the transfer probability of the observed areas. For instance, when spatial factors are disregarded, the probability of provinces transitioning from the L grade to the ML grade stands at 0.274. However, when considering the spatial lag condition, this transfer probability fluctuates between 0.234, 0.282, and 0.444.

Secondly, the development levels of neighboring regions exert varying impacts on the observed regions. In the traditional Markov transfer probability matrix, the transfer probability from the ML grade to the MH grade in the observed region is 0.269. However, this probability decreases to 0.182 when the average grade of neighboring provinces is L. Conversely, when the average grade of neighboring provinces is MH, the transfer probability increases to 0.429. This suggests that the positive spillover effect of high-value provinces outweighs the negative spillover effect of low-value provinces.

Lastly, the absence of provinces in the neighboring grade H and the observation area L indicates that provinces at a lower level are not adjacent to those at a higher level. The spatial spillover of high-value provinces necessitates a “ladder” state, where ML and MH provinces play a pivotal role. This indirectly suggests that the HDCI has evolved into a club convergence phenomenon characterized by overall divergence and local convergence in space.

4.5 Analysis of driving factors for HDCI

4.5.1 Selection of driving factors and analysis of model credibility

Taking into account the requirements for high-quality development of CI and the current economic and social context, and building on existing research (Ali et al., 2019a; Liu et al., 2023; Xu et al., 2022), this paper selects financial support (measured by the proportion of fiscal expenditure on science and technology, energy conservation, and environmental protection), urbanization process (quantified by the proportion of urban population), technological innovation capability (represented by the number of authorized patents), and energy consumption structure (reflected in the proportion of electricity and natural gas in CI energy consumption) as potential influencing factors for the high-quality development of the construction industry.

To mitigate the impact of magnitude and errors resulting from multiple covariance and heteroskedasticity, the proxy data for the selected factors were log-transformed before performing correlation analysis using SPSS software. The results revealed that the variance inflation factor (VIF) for each variable was less than 2, indicating the absence of significant multicollinearity. Furthermore, the non-smoothness test yielded goodness-of-fit values \(R^{2}\) of 0.635, 0.750, and 0.811 for the Ordinary Least Squares (OLS), Geographically Weighted Regression (GWR), and Geographically and Temporally Weighted Regression (GTWR) models, respectively. Correspondingly, the AIC values were -743.033, -799.987, and -817.391, respectively. These results suggest that the GTWR model offers the highest credibility in analyzing the spatio-temporal heterogeneity of factors influencing the high-quality development of the construction industry.

4.5.2 Result analysis

From the perspective of the global coefficients of influencing factors (Table 4), the parameter estimation results of the four explanatory variables vary, indicating that there are spatio-temporal differences in the impact of each explanatory variable on the development level of the construction industry. The order of influence from greatest to least is: technological innovation capability > urbanization process > financial support > energy consumption structure. Using ArcGIS software and the natural break method, the coefficients of each variable were visualized (Fig. 6) to further examine the spatial heterogeneity of their influencing effects.

  1. (1)

    The overall regression coefficient for financial expenditure is positive, indicating a promotional effect of fiscal support on the high-quality development of the construction industry. The spatial distribution of this coefficient exhibits a pattern of "high in the north and low in the south" when considering the influence of fiscal support on HDCI. Specifically, regions such as Beijing-Tianjin-Hebei and Northeast China have coefficients ranging from 0.0459 to 0.0748, indicating significant improvement in HDCI due to fiscal expenditure. The government can continue to guide financial investments in science and technology, energy conservation, and environmental protection to foster green and intelligent development in the CI (Lan et al., 2023; Yue et al., 2024). Conversely, in regions like South China, Xinjiang, Qinghai, and others, although financial expenditure on science and technology and environmental protection accounts for a considerable proportion, its supporting effect on HDCI is not as significant. This suggests a weaker collaboration between these expenditures and CI development in these regions, necessitating further enhancement of fiscal expenditures' guiding role in promoting green and low-carbon construction industry development.

  2. (2)

    The positive overall regression coefficient for the urbanization process indicates that the concentration of population in cities and towns remains beneficial for the HDCI. Spatially, the impact of urbanization on CI development exhibits a gradient decrease from west to east, resulting in distinct regions of promotion and inhibition. The promotional regions, primarily located in central and western provinces, are characterized by weak industrialization foundations and relatively low urbanization levels (Lang & Ding, 2023). The spatial agglomeration of urban population elements can effectively reduce pollutant and carbon emission intensities (Clark, 2013), fostering urban green development. Additionally, population urbanization enhances labor knowledge and skills, promoting the formation and accumulation of human capital (Amer et al., 2022). This provides fundamental conditions for the innovation and promotion of environmental protection technology, ultimately contributing to the enhancement of green total factor productivity in the construction industry. Conversely, the inhibition zone comprises the northeast and the majority of eastern coastal provinces. In these regions, the increase in urban population has an adverse effect on the HDCI. The urbanization process in the eastern and northeastern regions is at a mid to late stage (Liu et al., 2022). Excessive population influx not only leads to a decline in the marginal benefit of human capital but also gives rise to urban issues such as land resource scarcity, housing supply–demand imbalances, and environmental pollution (Ali et al., 2019b; Cheng & Yu, 2023; Yao et al., 2020). These factors have a negative impact on the sustainable development of the construction industry.

  3. (3)

    The overall regression coefficient for the energy consumption structure exhibits a positive and upward trend, indicating its progressively increasing beneficial impact on the HDCI. Spatial heterogeneity is evident in the coefficients, with higher values concentrated primarily in the eastern and central provinces, and lower values observed in the northeast. Notably, the Northeast region has historically served as a major base for heavy industrial development in China (Zhang et al., 2021). However, the region's lack of industrial modernization has resulted in a narrow energy consumption structure heavily reliant on coal and oil energy sources (Liao et al., 2020; Nie et al., 2019). This dependence has hindered industrialization progress and is detrimental to the HDCI. In contrast, provinces such as Jiangsu and Zhejiang in the east have been pioneers in adopting comprehensive new energy devices and equipment during production and operation stages. This has enabled the utilization of clean energy and electrification transformation, allowing these provinces to fully harness the environmental benefits associated with the optimized energy structure (Abbas et al., 2024; Yin & Hu, 2023). Consequently, these provinces are leading the way in realizing the benefits of sustainable energy consumption patterns and driving forward green development in China.

  4. (4)

    Science and technology innovation have the most significant impact on the HDCI, emphasizing its pivotal role in elevating productivity and competitiveness of enterprise products. Nationwide, the regression coefficient demonstrates a positive correlation, with a spatial distribution pattern exhibiting higher values in the east and lower values in the west. Given the advanced economic development in the eastern and central regions, augmented investment in scientific research can foster the development and implementation of novel energy and technology (Asghar et al., 2024; Lamnatou et al., 2022). Technological innovation can propel the green and low-carbon transformation of traditional industries, foster the growth of emerging industries, and cultivate fresh momentum for high-quality development (Jiakui et al., 2023; Zou, 2024). It reinforces the resilience of the industrial chain and supply chain internally, promotes the deep integration of digitization and greening externally, and offers comprehensive support for the achievement of the "double carbon" goal (Zhang et al., 2023b). However, for the western region, which has a weaker economic and social foundation and a scarcity of emerging industries, the time-lag effect of scientific research investment (Gao & Suo, 2018) results in relatively smaller outputs of scientific and technological achievements and a lower conversion rate. This limits the impact on high-quality development in the regional construction industry. Therefore, to effectively promote the modernization of the construction industry in the short term, western provinces should consider alternative strategies, such as actively seeking cooperation and introducing innovative processes.

Table 4 Regression coefficient of driving factors
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Fig. 6
figure 6

GTWR regression coefficient distribution

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5 Conclusion and policy implications

5.1 Conclusion

Since the reform and opening up in 1978, China's construction industry has rapidly expanded in scale, with a continuous increase in industry output value. However, the development of the industry has been accompanied by excessive resource consumption, low production efficiency, and environmental degradation. In light of this, a deep understanding of the quality of development in the construction industry across Chinese provinces is crucial for guiding carbon neutrality and sustainable industry development. Based on data from 30 provinces in China from 2008 to 2020, a high-quality development evaluation system for the construction industry was established. Using a combination of composite weighting, exploratory spatial data analysis, Markov chains, and GTWR models, the study explores the spatiotemporal differentiation patterns, evolutionary trends, and influencing factors of high-quality development in the construction industry. The main conclusions are as follows:

  1. (1)

    The high-quality development index of the construction industry shows an upward trend. Notably, the green energy-saving index emerges as the highest, underscoring a strong commitment to sustainability. In contrast, the comprehensive benefit index remains the lowest, highlighting areas in need of further improvement. The indices related to openness, coordination, and sharing the fruits of development with the people have made steady progress. Meanwhile, those for innovation-driven development and scale expansion have seen significant fluctuations. In the future, more resources should be invested in innovation, and attention should be paid to improving the modernization level of the construction industry chain and promoting the improvement of quality and efficiency of the construction industry production.

  2. (2)

    In regional terms, the central and eastern areas of China display strong development momentum, in stark contrast to the western and northeastern regions, where the average annual HDCI falls below the national average. Looking at the situation from an inter-provincial angle, there's a clear eastward expansion of provinces at a medium development level, accompanied by an increase in the number of provinces at higher and high levels of development. There is still much room for improvement in the high-quality development of the construction industry on the whole, with prominent regional imbalances.

  3. (3)

    The spatial epicenter of high-quality development in China's construction industry is shifting towards the southwest. HDCI shows pronounced spatial correlation, with the Markov probability transfer matrix indicating that the positive spillover effects from high-value provinces surpass the negative impacts from low-value provinces. How to break the regional barriers and give full play to the positive spillover effects among regions and provinces is the key to the development of China's construction industry to a higher level.

  4. (4)

    Analysis using the GTWR model sheds light on the driving factors of high-quality development in the construction industry. Crucially, financial support, the urbanization process, the structure of energy consumption, and the capacity for scientific and technological innovation stand out as pivotal. Nevertheless, the impact of these factors varies across different areas, mirroring the distinct challenges and opportunities that diverse regions encounter.

In the context of carbon neutrality, conducting research on the evaluation and enhancement pathways for high-quality development in the construction industry holds significant theoretical and practical implications. By exploring the essence, regional differences, and pathways of high-quality development in the construction sector, we can deepen our understanding of the concept of high-quality development within this industry, offering theoretical insights and benchmarks for the development of other sectors. Furthermore, the pursuit of high-quality development in construction intersects with multiple disciplines, such as architecture, engineering, economics, and management. Engaging in such multifaceted research encourages interdisciplinary collaboration, leading to the development of holistic research methodologies and theoretical frameworks. In practical terms, a comprehensive assessment of the construction industry's overall performance can reveal its strengths and weaknesses, aiding in the formulation of targeted improvement suggestions. This, in turn, supports the industry's broader goals of embracing green, low-carbon, and transformative objectives.

5.2 Policy implications

In conclusion, the findings of this paper will drive the low-carbon transition in the construction industry and promote sustainable development. As the constraints on scaling growth, adopting innovation-driven strategies, enhancing overall efficiency, and implementing green energy-saving practices intensify, there's a pressing need to accelerate the adoption of an innovation-driven approach to industrial development. This involves seizing the opportunities presented by the latest technological advancements, bolstering talent protection policies, prioritizing research and development investments across all stages, fast-tracking the integration of digital construction technologies, and aspiring to cultivate a digital economy that bolsters the high-quality development of infrastructure. Furthermore, it's critical to elevate design standards and regulations for green buildings, reinforce mechanisms for low-carbon and environmental oversight, and improve the enforcement and penalties associated with polluting and energy-intensive operations. Introducing financial support measures, such as tax incentives and exemptions, is also vital to foster green technological innovations within construction firms. By actively venturing into green building markets—including ultra-low-energy buildings, energy efficiency retrofits for existing structures, and modular construction—the construction industry can achieve stable growth in scale, efficiency, and sustainability.

In Eastern regions, such as Jiangsu and Beijing, high-quality development in the construction industry has set a benchmark. To sustain their leading positions, these regions need to amplify their focus on independent innovation and cultivate networks for regional cooperation and support. Such initiatives will encourage the sharing of industrial resources, technologies, and expertise in construction, generating beneficial spatial spillover effects. Centrally positioned provinces like Hubei and Henan are uniquely placed to foster partnerships with their developed neighbors, including forefront construction enterprises. Embracing technology-intensive and eco-friendly projects will not only optimize the allocation of innovation resources but also ensure their effective conversion into real-world benefits. Moreover, as pivotal regional connectors, areas such as Sichuan and Chongqing are positioned to facilitate and propel development across the east–west divide, fostering a pattern of development that is both synergistic and complementary. Exploiting the "One Belt, One Road" initiative, it's crucial to forge extensive networks for the construction sector, weaving geographically remote provinces such as Heilongjiang, Xinjiang, and Yunnan into a cohesive spatial development framework. This approach will enhance the interconnectedness of these regions and safeguard the continuity and stability of high-quality development within the construction industry.

Considering the spatial variations in financial support, urbanization progress, energy consumption structure, and scientific and technological innovation capabilities, it is advisable to implement policies tailored to the specific needs of each region. For areas like Beijing, Tianjin, Hebei, and Northeast China, effectively utilizing government financial resources to guide and enhance investments in science, technology, energy efficiency, and environmental protection can significantly improve the construction industry's technical expertise and competitiveness. This approach will direct its development towards more green, intelligent, and sustainable practices. In contrast, regions that are currently behind in scientific and technological innovation, such as Xinjiang, Qinghai, and Yunnan, should focus on leveraging urban planning and infrastructure demands to bolster the construction of green infrastructure and enhance the provision of ecological services, thereby accelerating urbanization. These areas are encouraged to adopt policies, investments, and initiatives that integrate new energy sources, advanced equipment, and innovative construction methods. Such measures will facilitate the broad adoption of technological innovations in the construction sector, leading to greater mechanization and modernization. While the influence of energy consumption structure on the HDCI is less pronounced in the northeast and southwest, the promotion of renewable energy sources and the initiation of building electrification projects remain crucial. These efforts should be supported by strong systems and a strict adherence to the rule of law. Moreover, improving the use of clean coal and focusing on the high-quality refinement and deep processing of coal are vital steps. Allocating resources strategically to these areas will not only have a greener impact but also stimulate industrial transformation and upgrading through technological innovation, ultimately enhancing the construction industry's quality and efficiency.

5.3 Study limitations

The empirical findings of this study can guide other countries or regions seeking coordinated development in the construction industry, focusing on economic benefits, environmental protection, and social welfare, to formulate effective intervention measures for sustainable development. However, there are limitations that merit further investigation. Firstly, the selection of evaluation indicators is hindered by data availability, thus restricting the inclusion of crucial factors. Future studies should consider indicators that better capture the development quality of the construction industry, such as green building area, BIM, and the utilization of digital technology. Secondly, while this study computes the index of six dimensions of high-quality development in the construction industry, it lacks a granular analysis of each dimension's progress across all provinces. Thirdly, the study only delves into the spatial heterogeneity of four driving factors, such as financial support. Future research should adopt more appropriate methods or models to identify factors with stronger explanatory power. Furthermore, this paper focuses exclusively on 30 provinces in mainland China. Incorporating developed countries into the comparative analysis would offer a more comprehensive perspective, highlighting conventional features and offering more precise guidance for promoting high-quality development in the construction industry, particularly in the context of carbon neutrality.