Introduction

The report of the 20th National Congress of the Communist Party of China (CPC) pointed out that it is necessary to accelerate the green transformation of the development mode, and actively and steadily promote carbon peaking and carbon neutrality. The Fourteenth Five-Year Plan proposes that priority should be given to energy conservation, especially in several areas such as construction, the development of intelligent construction, the promotion of green building materials, prefabricated assembled buildings and steel-framed dwellings, and the focus on the development of green buildings and the construction of low-carbon cities. In the context of new urbanization, the continuous increase in the area of building construction, operating hours and energy consumption demand has directly increased the scale of carbon emissions from the construction industry and its proportion in the total carbon emissions, leading to a doubling of the pressure on carbon emission (CE) reduction in the construction industry. The energy consumption of the construction industry exceeds one-fifth of the energy consumption of the whole society, and the growth trend is significant, energy saving and emission reduction potential is large, which is an important entry point to realize CE reduction.

Therefore, high-quality development (HQD) of the construction industry is the core driving force to reduce carbon emissions from the construction industry and realize the goal of “double carbon”, and the in-depth integration of HQD of the construction industry and CE reduction is the key to realizing a win-win situation for ecological environmental protection and the transformation and development of the construction industry. Since there are obvious differences in HQD and CE of the construction industry in China’s provinces and regions, whether CE can be harmonized with HQD of the construction industry is a real problem that needs to be explored and solved. Based on the principle of coupling coordination, this study measured the coupling coordination degree (CCD) between HQD-CE of the construction industry in China’s provincial areas, and on this basis, revealed the temporal and spatial evolution characteristics of the CCD, to promote the interaction and integration of them and reach synergistic optimization among provinces and regions, and then realize the comprehensive green transformation of the mode of economic development to provide a reference for decision-making.

Existing studies on CE mainly focus on measurement methods1, spatial and temporal evolution2,3,4 and influencing factors5,6. The CE factor method7,8, the life cycle method9 and the input-output method10,11 are mostly used. In the study of CE influencing factors, the STIRPAT model12, exponential decomposition method13 and structural decomposition method14 are mostly used; some scholars found that factors such as population15, technology16, industrial structure17 and government intervention18 correlate with the intensity of CE; some scholars start from the industrial level19,20,21 or focus on the administrative region22,23,24,25,26 to explore the spatial and temporal changes of CE. The research on HQD of the construction industry mainly focused on the connotation, the construction of the evaluation index system and the analysis of influencing factors, etc. Gao and Wang27 constructed the evaluation index system for HQD of the construction industry from five dimensions based on the concept of HQD of the economy, combined with the characteristics of the development of the construction industry in China. Wang and Wu28 included CE as a non-desired output in the evaluation system and investigated the characteristics of spatial and temporal differentiation, the evolution of dynamic trends, and their drivers of HQD in China’s construction industry. Song et al.29 and Wang et al.30 studied the influencing factors of green building development at the national and municipal levels, respectively.

By reviewing existing literature, it can be found that scholars currently have in-depth research on the subfields of HQD and CE of the construction industry, but there are not yet many studies on the relationship between the two, and there are fewer studies on the relationship between the two coupled and coordinated development. Related studies are more common to analyze the countermeasures and paths for HQD of the construction industry under the low-carbon goal31,32. In this paper, based on clarifying the coupling and coordination mechanism between HQD and CE of the construction industry, 30 provinces in China are taken as the research objects, the CCDM is constructed, and the dynamic evolution trend of the coupling and coordinating of the two systems is investigated with the help of the spatial autocorrelation and the Markov chain. Compared to previous literature, this paper has made the following innovations and contributions. First of all, this paper establishes the HQD system for the construction industry from six aspects: economy, innovation, green, coordination, sharing and openness, which enriches the existing evaluation index system for HQD of the construction industry. Secondly, realizing the goals of carbon peaking and carbon neutrality requires not only controlling carbon emissions but also improving the efficiency of carbon emissions. Therefore, unlike the construction of CE systems in the previous literature, this study evaluates CE in terms of both the efficiency of carbon emissions and the current status of CE. Finally, the temporal and spatial evolution characteristics of CCD of the HQD-CE system in the construction industry are analyzed from both static and dynamic perspectives, which provides a reference for the scientific formulation of CE reduction programs in the construction industry.

The remaining parts of this research are as follows: Sect. 2 explains the mechanisms of coupled coordination relationships; Sect. 3 introduces the research methodology; Sect. 4 and Sect. 5 demonstrate the empirical results and discussion; Sect. 6 draws conclusions, provides policy implications and points out limitations.

The mechanism of coupling coordination

The HQD-CE of the construction industry is an open system with rich content and wide coverage, and the elements constituting the system interact and influence each other. It is of great significance to analyze the interaction mechanism between the two for the sustainable and high-quality development of the construction industry and the achievement of the “dual-carbon” goal.

On the one hand, China is gradually promoting the transformation of the green and HQD of the construction industry. China has increased policy and financial support, guided all sectors to allocate funds and other elements to green industries, accelerated the research and development of energy-saving and carbon-reducing advanced technologies, and contributed to the improvement of CE efficiency in terms of funding and technology; vigorously promoting green and low-carbon buildings, optimizing energy-saving and carbon-reducing design of newly built buildings, advancing energy-saving renovation of existing buildings, and establishing a regulatory system for energy-saving in public buildings to reduce carbon emissions are important drivers for the realization of the “dual carbon” goal. On the other hand, under the crisis of the unsustainable traditional development mode, taking CE as the general grip, breaking the traditional path dependence of high energy consumption and high emission, forcing the transformation and upgrading of the economic structure, energy structure and industrial structure, and continuously empowering HQD of the construction industry. The mechanism of the coupling and coordination relationship of HQD-CE in the construction industry is shown in Fig. 1.

Fig. 1
figure 1

The mechanism of coupling coordination.

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Materials and methods

Comprehensively analyzing HQD and CE of China’s construction industry, this paper argues that the two systems have a complex coupling relationship and are constantly changing in time and space dimensions. Therefore, this paper constructs a coupling coordination framework and evaluation index system to measure the development level of the two systems, and adopts a spatial autocorrelation model and Markov chain to portray its spatial evolution characteristics.

The evaluation indicator system

High-quality development of construction industry indicator system

Regarding the connotation and characteristics of HQD in the construction industry has not yet formed a unified standard, and the existing studies have generally taken the five development concepts of “innovation, coordination, green, openness and sharing” as the evaluation criteria for HQD. According to the overall requirements and main tasks of The Fourteenth Five-Year Plan, combined with the connotation and characteristics of HQD of the construction industry, and based on the principles of scientific, systematic and accessible, and with reference to the studies of Cao et al.33, Gao & Wang27, Zhang & Zhang34 and Zhang et al.35, the index system of HQD of the construction industry constructed in this paper includes a total of 19 indexes in six aspects, including economic efficiency, innovative development, green development, coordinated development, shared development, and open development. As shown in Table 1.

Table 1 High-quality development (HQD) of the construction industry evaluation index system.
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Carbon emission of construction industry indicator system

To realize the goal of carbon peak and carbon neutrality, it is not only necessary to control carbon emissions but also to improve carbon emissions efficiency. Applying the ‘Energy Indicators for Sustainable Development: Guidelines and Methodologies’, jointly published by the International Atomic Energy Agency and the United Nations Department of Economic and Social Affairs as a reference, combined with the studies of Chen et al.36, Fu et al.37 and Jiang et al.38, the carbon emission index system of the construction industry was established, including 2 aspects of carbon emission efficiency and carbon emission status. Efficiency indicators are used to reflect the internal performance of the CE system, while status indicators are used to reflect the external performance. The specific indicators are shown in Table 2.

Table 2 Carbon emission (CE) of construction industry evaluation index system.
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Data sources

The relevant data from 30 provinces in China from 2012 to 2021 are collected for the study (Tibet, Hong Kong, Macao and Taiwan were not included in the study due to a large amount of data missing). The indicators data are mainly from China Statistical Yearbook (2013–2022), China Construction Industry Statistical Yearbook (2013–2022), China Energy Statistical Yearbook (2013–2022) of the National Bureau of Statistics of China, as well as the local statistical yearbook and China Carbon Accounting Database. For very few missing data, the linear interpolation method is used to complete. 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, as shown in Fig. 2.

Fig. 2
figure 2

China’s four major regions and provincial administrative divisions.

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Research methodology

The entropy method

The Entropy method is an objective empowerment method, which determines the indicator weights according to the observed values of each indicator, which can avoid the interference brought by human factors to the greatest extent, and make the measurement of HQD and the level of CE of the construction industry more reasonable. The specific steps are as follows:

Standardization of data:

Positive indicator

$${Z_{ij}}=\frac{{{X_{ij}} - \hbox{min} {X_{ij}}}}{{\hbox{max} {X_{ij}} - \hbox{min} {X_{ij}}}}$$
(1)

Negative indicator

$${Z_{ij}}=\frac{{\hbox{max} {X_{ij}} - {X_{ij}}}}{{\hbox{max} {X_{ij}} - \hbox{min} {X_{ij}}}}$$
(2)

where Zij is the standard value, if Zij is 0, it is replaced by 0.00001. Xij represents the value of the indicator j of the system i, max Xij and min Xij represent the maximum and minimum values of the indicator Xij, respectively.

Calculation of the weights of the indicators:

$${p_{kj}}=\frac{{{Z_{ij}}}}{{\sum\limits_{{i=1}}^{n} {{Z_{ij}}} }}$$
(3)
$${e_j}= - \frac{1}{{\ln (n)}} \cdot \sum\limits_{{i=1}}^{n} {{p_{kj}}} \ln ({p_{kj}})$$
(4)
$${w_j}=\frac{{1 - {e_j}}}{{n - \sum\nolimits_{{j=1}}^{n} {{e_j}} }}$$
(5)

where pkj is the weight of the indicator j in the province k, n is the number of provinces, ej is the entropy value of the indicator j and wj is the weight of the indicator j.

Calculation of a comprehensive system development index:

$${U_i}=\sum\nolimits_{{i=1}}^{n} {{w_j}} {Z_{ij}}$$
(6)

Coupling coordination degree model

In this paper, the degree of interconnection between the HQD-CE systems of the construction industry is defined as the degree of coupling, and the degree of coordinated development of the two systems is defined as the coupling coordination degree (CCD). Referring to the existing studies, the coupling model is introduced to analyze the CCD of HQD-CE systems of the construction industry. In order to avoid the problem of underestimation of the coupling degree, the calculation formula is as follows:

$$C=\frac{{2\sqrt {{U_1} \times {U_2}} }}{{{U_1}+{U_2}}}$$
(7)
$$D=\sqrt {C \times T}$$
(8)
$$T=\alpha {U_1}+\beta {U_2}$$
(9)

Where C represents the coupling degree; D represents CCD, ranging from 0 to 1; T is the degree of integration of the two systems; α and β represent the weights of the two systems; U1 and U2 represent HQD and the level of CE in the construction industry, respectively. Considering that the HQD system is as equally important as the CE system, both α and β are assigned a value of 0.5, and the coupling degree and the CCD are graded. In order to make the coupling coordination value informative, based on the research of Li et al.39 and combined with the actual measurement results, this paper divides the CCD into four grades, as shown in Table 3.

Table 3 Classification of coupling coordination degree (CCD).
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Spatial autocorrelation analysis

In view of the fact that the CCD of HQD-CE of the construction industry may have a clustering state at the provincial spatial level, this paper introduces the global Moran’s I to test the spatial correlation, and the formulas are as follows:

$$I=\frac{{\sum\limits_{{i=1}}^{n} {\sum\limits_{{j=1}}^{n} {{W_{ij}}\left( {{X_i} - \overline {x} } \right)\left( {{X_j} - \overline {x} } \right)} } }}{{{S^2}\sum\limits_{{i=1}}^{n} {\sum\limits_{{j=1}}^{n} {{W_{ij}}} } }}$$
(10)
$${S^2}=\sum\limits_{{i=1}}^{n} {\left( {{X_j} - \overline {x} } \right)}$$
(11)

where Xi and Xj represent the coupling coordination degree of HQD of the construction industry and carbon emission in each province, \(\bar {x}\) is the mean value, n is the number of provinces, and Wij is the spatial weight matrix. Moran’s I is used to measure the global spatial pattern characteristics of the CCD, ranging from − 1 to 1, and positive values indicate positive spatial correlation, negative values indicate negative spatial correlation, and equal to 0 indicates that its spatial distribution is in a random state, with no obvious correlation.

The global Moran’s I is mainly used to explore the spatial correlation of the region as a whole, but it is easy to ignore the differentiated characteristics within the region, which in turn leads to the homogenization of the research results40. For this reason, it is necessary to introduce the local Moran’s I to examine the local spatial autocorrelation of the CCD of HQD-CE in the construction industry, with the formula:

$${I_i}=\frac{{\left( {{X_i} - \overline {x} } \right)}}{{{S^2}}}\sum\nolimits_{{j=1}}^{n} {{W_{ij}}} \left( {{X_j} - \overline {x} } \right)$$
(12)

where Xi, Xj, \(\bar {x}\), n, S2, Wij are consistent with the settings in the global Moran’s I. A positive value of Ii indicates that a province’s coupling coordination is positively correlated with neighboring provinces, while a negative value of Ii indicates a negative correlation.

Spatial Markov chain

Due to the differences in geographic location, resource endowment and economic development level, the CCD of HQD-CE of the provincial construction industry may have regional correlation and dependence in a geographical sense, and this paper tries to explore this issue by introducing Markov chains. The CCD is regarded as a discrete time series and discretized into three types.

Let \({M_t}=\left[ {{M_{1,t}},{M_{2,t}},{M_{3,t}}, \ldots ,{M_{k,t}}} \right]\) be the probability distribution vector of each state at the moment t, then the transfer between different moments can be regarded as a Markov state transfer probability matrix of order \(k * k\), as shown in Eq. (13).

$$P=\left[ {\begin{array}{*{20}{c}} {{p_{11}}}& \cdots &{{p_{1k}}} \\ \vdots & \ddots & \vdots \\ {{p_{k1}}}& \cdots &{{p_{kk}}} \end{array}} \right]$$
(13)

where Pij represents the probability that the coupled coordination of HQD-CE in the construction industry shifts from type i at moment t to type j at moment t+1; \({P_{ij}}={a_{ij}}/{a_i}\), aij represents the sum of the number of provinces that shifted from type i at moment i to the state of j at moment t+1 in the study period, and ai represents the sum of the number of provinces that belong to type i in the study period.

The change of the coupled coordination state of HQD-CE in China’s provincial construction industry is not an isolated spatial evolution process but is in a domain environment with state characteristics, which is influenced by the coupled coordination state of the neighboring domains and has spatial spillover effects. Therefore, the concept of “spatial lag” is introduced into the traditional Markov chain to obtain a spatial Markov chain, and the influence of the CCD of neighboring provinces on the dynamic evolution of the coupling coordination state of the province is examined. The spatial Markov matrix, conditional on the spatial lag type of the base period year (assuming that there are k spatial lag types), decomposes the ordinary Markov matrix of order k * k into k k * k conditional transfer probability matrices, and Pij represents the probability of transferring from the type i in the year t to the type j in the year t+1 conditional on the spatial lag type k of a certain province at the moment of t. This will reveal the dynamic role of spatial factors in the coupled coordinated state transfer process.

Results

Analysis of the HQD system of the construction industry

Figure 3 shows the temporal trend of the comprehensive index of the HQD system of China’s construction industry. The HQD system composite index increased from 0.2629 in 2012 to 0.4102 in 2021, with an average annual growth rate of 5.6%, indicating an overall positive trend in HQD of the construction industry.

Fig. 3
figure 3

Temporal evolution trend of HQD of China’s construction industry from 2012 to 2021.

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The spatial pattern of the HQD system of the construction industry in 2012, 2015, 2018 and 2021 is presented in Fig. 4, separating the composite index of the HQD system into four levels, from low to high. In 2012, HQD of the construction industry was concentrated at a higher level in central and eastern coastal areas, while it was at a lower level in western and northeastern regions. The provinces with a high level of HQD of the construction industry were mainly concentrated in central and eastern coastal areas, while it was at a low level in western and northeastern regions. Zhejiang, Jiangsu, Beijing, Tianjin, Guangdong, Shanghai, Hebei, Hunan, Jiangxi, Shaanxi, Shandong and Liaoning were at the second level. It is worth noting that the composite index of HQD of the construction industry in Liaoning and southeast coastal provinces was at the same level. However, the composite index of HQD of the construction industry in Liaoning is 0.2533, while the southeast coastal provinces are about 0.4. there is still a gap between the two. In 2021, the southeast coastal areas remained higher than the inland provinces. Jiangsu was at the fourth level with a composite index of 0.8204. Jiangsu was at the fourth level with a composite index of 0.8204, Beijing, Hubei, Guangdong and Zhejiang were at the third level, Heilongjiang was at the first level, and the remaining provinces were at the second level. Beijing, Hubei, Guangdong and Zhejiang are at the third level, Heilongjiang is at the first level, and the other provinces are at the second level. In general, the level of HQD of the construction industry in 30 provinces has progressed significantly, but there is an imbalance in regional development.

Fig. 4
figure 4

Spatial pattern of HQD of China’s construction industry in 2012, 2015, 2018 and 2021.

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Analysis of the CE system of the construction industry

Figure 5 presents the temporal trend of the composite index of the CE system of China’s construction industry. The CE system composite index was less volatile, with the maximum and minimum values of 0.2464 in 2021 and 0.2064 in 2012, respectively. The eastern, central and western regions showed similar trends. However, the comprehensive index of the CE system in the northeast region fluctuated greatly from 2013 to 2019, exhibiting a development trend of “down-up-down”, with no significant change in the front and back levels.

Fig. 5
figure 5

Temporal evolution trend of CE of China’s construction industry from 2012 to 2021.

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The spatial pattern of the CE system of the construction industry in 2012, 2015, 2018 and 2021 is presented in Fig. 6, separating the comprehensive index of the CE system into four levels, from low to high. In 2012, the higher-level provinces were mainly concentrated in the eastern region, and the lower-level provinces were primarily distributed in the central and western regions. Hainan, Guangxi, Jiangsu, Heilongjiang, Beijing, Jiangxi and Zhejiang were at the second level, while other provinces were at the first level. In 2021, Jiangsu was at the fourth level, most provinces in the eastern region were at the second level, while most provinces in the central and western regions were still at the first level. In addition, the comprehensive index of the CE system of Tianjin, Inner Mongolia, Jilin, Heilongjiang, Zhejiang and Anhui showed a decreasing trend. Overall, differences in the CE system of the construction industry are evident among provinces, and the eastern provinces were generally higher than the provinces in the central and western regions.

Fig. 6
figure 6

Spatial pattern of CE of China’s construction industry in 2012,2015,2018 and 2021.

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Analysis of coupling coordination degree

Then, the CCD between HQD-CE systems of the construction industry in 30 provinces in China from 2012 to 2021 is calculated based on the CCDM. As shown in Fig. 7, the average value of the CCD of HQD-CE of the construction industry increased from 0.4744 in 2012 to 0.5416 in 2021. The CCD in all of China’s 30 provinces showed a fluctuating upward trend, indicating that the coordination relationship of the HQD-CE systems of the construction industry in these regions was continuously improved, and the green, low-carbon and high-quality development of the construction industry was gradually promoted. Four years (2012, 2015, 2018 and 2021) were selected to analyze the evolution trend of the CCD of HQD-CE of the construction industry, as shown in Fig. 8.

Fig. 7
figure 7

Temporal evolution trend of CCD from 2012 to 2021.

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Fig. 8
figure 8

Spatial distribution of CCD in 2012,2015,2018 and 2021.

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In 2012, none of the provinces reached the high-quality coupling coordination stage, Beijing, Tianjin, Heilongjiang, Jiangsu, Zhejiang, Jiangxi, Guangxi and Hainan were in the basic coupling coordination stage, and the values of CCD of the remaining provinces were in the basic coupling dissonance stage, and no province was in a stage of serious coupling dissonance. In 2015, Beijing, Jiangsu, Zhejiang, Jiangxi, Guangxi and Hainan were still in the basic coupling coordination stage, Hubei transitioned from basic coupling dissonance to basic coupling coordination, and the values of CCD of the remaining provinces were in a basic coupling dissonance state, which changed little compared with 2012. In 2018, Jiangsu took the lead in achieving a breakthrough, becoming the first province in the country to reach a high-quality coupling coordination state, and Liaoning, Shandong, Shanghai, Fujian, Guangdong, and Chongqing transformed from basic coupling dissonance to basic coupling coordination. In 2021, the values of CCD improved, and Beijing became the second province to reach the high-quality coupling coordination stage, Hebei, Anhui, Hunan, Shaanxi and Sichuan transformed from basic coupling dissonance to basic coupling coordination, and the rest of the provinces are still in the basic coupling dissonance stage.

Overall, the CCD of HQD-CE of the construction industry in China’s 30 provinces has improved on the whole, showing a spatial distribution and evolution pattern of high in the southeast, low in the northwest, and decreasing from the coastland to the inland.

Spatial correlation analysis of coupling coordination degree

From 2012 to 2021, the global Moran’s I measurement results of the CCD of HQD-CE in the construction industry are shown in Table 4. The Moran’s I of all years are positive, and except for 2013, the global Moran’s I of the rest of the years are significant at the 10% level, showing obvious spatial positive correlation.

Table 4 Global spatial autocorrelation of CCD.
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Furthermore, in this research, the local spatial features of CCD are categorized into four kinds of spatial associations: high-high (H-H), low-high (L-H), low-low (L-L), and high-low (H-L), as shown in Fig. 9.

Fig. 9
figure 9

  Local spatial autocorrelation of CCD.

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A H-H cluster indicates high CCD in an area and adjacent areas. In 2012, Beijing, Tianjin, Jiangsu, Zhejiang, Jiangxi and Hunan were all in this category. In 2021, the number of provinces with H-H clusters increased to eight, including Shanghai, Fujian, Hubei, Hunan, Guangdong and so on. A L-H cluster indicates low CCD in an area, with high CCD in adjacent areas. In 2012, Shanghai, Anhui, Fujian, Shandong, Hubei and Guangdong were in this category. In 2021, Tianjin, Anhui, Shandong, Henan and Hainan were in this category, showing signs of shifting from the southeast to the central region. A L-L cluster is low CCD in an area and adjacent areas. In 2012, L-L clusters included Hebei, Shanxi, Inner Mongolia, Henan, Chongqing, Sichuan and so on. In 2021, L-L clusters mainly included Jilin, Heilongjiang, Guizhou, Yunnan, Shaanxi, Gansu, etc., appearing phenomena transferring from the central region to the northeast. A H-L outlier is high CCD in this area, with low CCD in adjacent areas. Liaoning, Heilongjiang, Guangxi and Hainan were in this category in 2012, and Beijing, Hebei, Liaoning, Guangxi and Chongqing were in this category in 2021. On the whole, it can be seen that most of the provinces in 2012 and 2021 showed the characteristics of H-H agglomeration or L-L agglomeration. The H-H cluster areas are concentrated in the economically developed areas in the east, while the L-L cluster areas are mostly concentrated in the western areas. Thus it can be seen that the homogeneous agglomeration effect is always dominant, and the spatial evolution pattern is relatively stable.

Markov chain transfer probability analysis for CCD

Referring to the study of Chen et al.41, the traditional Markov probability transfer matrix is introduced to explore the evolutionary characteristics of CCD. The results are shown in Table 5, the probability values on the diagonal are much larger than other probability values in the same row, which indicates that each type of CCD is biased to maintain the original state unchanged when the state transition occurs, and has obvious “club convergence” characteristics. The convergence characteristics of high-quality coupling coordination are more obvious, and there is a “high-level monopoly” phenomenon.

Table 5 Markov transition probability matrix from 2012 to 2021.
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The calculation results of the spatial Markov chain probability transfer matrix are given in Table 6. By comparing with the conventional Markov transfer probability matrix, these results can be obtained. When the neighborhood context of each region is considered, the transition probability of CCD shows a significant change. The probability on the diagonal is still the maximum in the same row, which indicates that the probability of inter-type transition of CCD is low. There is a spatial spillover effect of CCD, and the probability of upward transfer increases significantly when a province is adjacent to a region with a high CCD level; conversely, the probability of upward transfer is suppressed when it is adjacent to a region with a low CCD level. For example, when the adjacent region of a type II province changes from type II to type III, the probability of its CCD shifting upward increases from 8.3 to 19.6%.

Table 6 Spatial Markov transition probability matrix of CCD from 2012 to 2021.
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Discussion

Spatial and temporal dynamic changes of HQD-CE systems in the construction industry

The level of HQD of China’s construction industry rose during the study period. Due to different development conditions (i.e. economy, innovation and openness)42, there are obvious regional differences in the HQD level of the construction industry. The eastern and central regions have been leading in comparison to the northeastern and western provinces, because of developed economies, sound infrastructure and strong policy support for science and technology innovation; however, the western and northeastern regions are economically backward, with lagging infrastructure construction and insufficient policy support.

From a general perspective, The values of the CE system in China’s construction industry had increased slightly, and the values were higher in the eastern than in the western region. The eastern region has introduced a series of carbon reduction policies and advocated the development of green and low-carbon buildings, while the western region is still in the development stage of urbanization, and the construction industry plays an important role in the process of urbanization43. From a provincial perspective, the values of the CE system of the construction industry in Hebei and Jiangsu have increased evidently. However, Tianjin has insufficient investment in scientific research and innovation, and Henan’s construction industry has a low level of technology and management, resulting in a significant reduction in the values of the two provinces. The values of the CE system of the construction industry in western provinces such as Inner Mongolia, Xinjiang and Gansu showed a decreasing trend, which is related to the accelerated promotion of infrastructure construction in western regions.

Spatial and temporal dynamic changes of CCD

From a temporal perspective, the average CCD between HQD-CE systems of China’s construction industry witnessed a steady growth, from 0.4744 in 2012 to 0.5416 in 2021, from the basic coupling dissonance stage to the basic coupling coordination stage. This result indicates that the green, low-carbon and high-quality development of China’s construction industry has achieved remarkable results. Various measures for HQD of the construction industry have improved the CE status, and the improvement of the CE status of the construction industry has promoted HQD, so the coupling and coordination level between the two have been optimized as a whole.

From a spatial perspective, the CCD of HQD-CE of the construction industry generally presents a spatial distribution of high in the southeast and low in the northwest and an evolution pattern of decreasing from coastland to inland, which is positively correlated with the economic development level of different regions in China. As an important region of China’s economic development, the eastern region has sufficient reserves of capital and talent and has a high level of technology and innovation ability. With the promotion of HQD, construction enterprises in the eastern region have responded to the national call for active transformation, low carbon and high-quality development routes. As an economically developed province in the east, Jiangsu plays a leading role in promoting the coordinated development of HQD-CE in the construction industry44. At present, the CCD of HQD-CE of the construction industry has been basic coupling coordination in most provinces, but the provinces located in the western region have been in the stage of basic coupling dissonance for a long time. The overall development of the construction industry in the western region started late, the infrastructure is backward, and the industrial structure is unreasonable, which leads to its poor economic level. At the same time, highly educated talents are more inclined to go to developed regions such as eastern China, resulting in a serious brain drain, and the western region lacks the impetus for scientific and technological innovation45. It is necessary to guard against these provinces becoming obstacles to HQD of the construction industry and the realization of the “double carbon” goal.

Conclusion and policy implications

Conclusion

Since the reform and opening up, China’s construction industry has continued to develop rapidly, accompanied by the rapid growth of carbon emissions. Therefore, an in-depth understanding of the HQD-CE development status of the construction industry in China is of great significance to guide the low-carbon and HQD of the construction industry and drive the achievement of the “dual carbon” goal.

Taking 30 provinces in China as the research object, this research analyzes the spatio-temporal evolution of the CCD of HQD-CE of the construction industry from 2012 to 2021 by using the CCDM, spatial autocorrelation model and Markov chain. The main conclusions are as follows: (1) The comprehensive level of the HQD system of the construction industry continued to improve, presenting high spatial characteristics in the central and eastern regions and low in the western regions, and it still needs to be further improved. (2) The comprehensive index of the CE system of the construction industry showed a slow upward trend and showed the spatial characteristics that the eastern coastal region were higher than the central and western regions. The CE system levels are the lowest in major coal-producing areas, such as Inner Mongolia. (3) The CCD of HQD-CE of the construction industry continues to rise, gradually transforming from “basic coupling dissonance” to “basic coupling coordination”, with obvious regional differences, showing the spatial distribution characteristics of eastern region > central region > northeastern region > western region. (4) The CCD has a significant spatial correlation, and the H-H clustering is mainly concentrated in the eastern region and has a trend of transferring to the central region; L-L clustering is mainly concentrated in the western region. (5) The CCD basically obeys the steady-state distribution, and the “club convergence” feature is obvious. The development evolution of CCD of each province is affected by the neighboring regions, with type II provinces facing the problem of low-level solidification, and type IV provinces being fewer in number and with insufficient driving effects.

Policy implications

In conclusion, it can provide guidance and suggestions for promoting the coordinated development of HQD-CE in the construction industry from the perspectives of government intervention, scientific and technological innovation and industrial structure. Strengthen top-level design, and establish a sound policy system for the coordinated development of HQD-CE in the construction industry. At present, there are significant differences in the coordination level between the HQD-CE systems of the construction industry in China’s 30 provinces, the resource allocation mechanism needs to be improved to promote the efficiency of inter-regional resource allocation. Policymakers should give appropriate preference to regions with low CCD levels, and avoid a “one-size-fits-all” approach to policy formulation. Local governments can establish cross-regional coordination organizations, build information technology resource platforms and encourage inter-regional resource co-construction and sharing to alleviate regional development imbalances. Especially, the western provinces with low CCD could pair with the eastern provinces to help each other and promote the coordination level of the western region.

In addition, adhere to innovative development, actively introduce and train high-quality technical personnel, increase research and development investment, increase the application of information technology in the construction industry, to improve the innovation driving force of the construction industry. Accelerate the transition of the construction industry structure, promote the application of new digital construction technologies, integrate Internet technology with the traditional construction industry, and optimize the management and operation of construction projects through information and intelligent means. In-depth implementation of the concept of green development is an important path for the construction industry to achieve low-carbon, high-quality coordinated development.

Study limitations

The empirical findings of this study can provide a reference for the policy formulation of the green, low-carbon and high-quality development of the construction industry. Nevertheless, this study has some limitations which we will share for future research. If more detailed data is available (such as prefecture-level cities), we will delve into more granular levels such as cities or regions in future studies. Second, the key indicators of the HQD-CE systems in the construction industry were identified but were not examined in depth in this study, and future research could further investigate the specific impact of key indicators on the CCD in each province, for example, through a geographically weighted regression model. Thirdly, there may be some simplifications in the weight setting of the CCDM, which will be improved in future research.