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Evaluation and Spatial Evolution Analysis of High-Quality Development in China’s Construction Industry Utilizing Catastrophe Progression Method: A Case Study of Twelve Provinces in the Western Region

School of Civil Engineering, Architecture, Environment, Xihua University, Chengdu 610039, China

School of Construction Engineering, Sichuan Technology and Business University, Chengdu 611745, China

College of Environment and Civil Engineering, Chengdu University of Technology, Chengdu 610059, China

Author to whom correspondence should be addressed.

Sustainability 2024, 16(24), 10879; https://doi.org/10.3390/su162410879

Submission received: 25 October 2024 / Revised: 7 December 2024 / Accepted: 9 December 2024 / Published: 12 December 2024

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Figure 1
Trends in the three major objectives of the construction industry [<a href="#B4-sustainability-16-10879" class="html-bibr">4</a>]. "> Figure 2
Growth rate and total output value of China’s construction industry from 2011 to 2020. Note: Data sourced from the National Bureau of Statistics of China: <a href="https://www.stats.gov.cn/sj/ndsj/" target="_blank">https://www.stats.gov.cn/sj/ndsj/</a> (accessed on 12 April 2024). "> Figure 3
Research flowchart. "> Figure 4
Evaluation model of HQDCI based on catastrophe theory. "> Figure 5
The total output value and growth rate of the construction industry in eastern and western regions of China from 2015 to 2019. Note: Data sourced from the National Bureau of Statistics of China: <a href="https://www.stats.gov.cn/sj/ndsj/" target="_blank">https://www.stats.gov.cn/sj/ndsj/</a> (accessed on 12 July 2024). "> Figure 6
Study area. "> Figure 7
HQDCI in western China in 2015, 2017, and 2019. "> Figure 8
Evaluation results for each dimension of HQDCI. "> Figure 9
The spatial distribution of the level of HQDCI in western China. Note: This map contains only the 12 regions of western China relevant to the research and is not a complete map of China. "> Figure 10
The spatial distribution of various dimensional levels of HQDCI. Note: This map contains only the 12 regions of western China relevant to the research and is not a complete map of China. "> Figure 11
Moran’s I for the overall goal of HQDCI. "> Figure 12
Moran’s I for various dimensional indicators of HQDCI. "> Figure 13
Scatter plot frame of HQDCI in western China. "> Figure 14
Scatter plot frame of dimensional indicators of HQDCI in western China. "> Figure 15
The results of LISA significance and cluster analysis of HQDCI in Western China in 2019. Note: This map contains only the 12 regions of western China relevant to the research and is not a complete map of China. "> Figure 16
The results of LISA significance analysis for various dimensions of HQDCI in western China in 2019. Note: This map contains only the 12 regions of western China relevant to the research and is not a complete map of China. "> Figure 17
The results of LISA cluster analysis for various dimensions of HQDCI in western China in 2019. Note: This map contains only the 12 regions of western China relevant to the research and is not a complete map of China. ">

Versions Notes

Abstract

With the spread of the concept of sustainable development, the quality of development of the construction industry has begun to receive attention. Compared with speed, the quality of the development of the construction industry is not only reflected in its output, but also its impact on socio-economic development factors, which should be emphasized, and the comprehensiveness of its measurement is more difficult to ensure. However, theoretical and practical research on construction development in developing countries has been limited, mainly in terms of the semantic foundations and quantitative methods of the subject. Therefore, this paper focuses on China, the largest developing country, defines the concept and connotation of high-quality development of the construction industry (HQDCI), and constructs a set of tools for evaluating and analyzing HQDCI based on the theory of mutation and the relevant theories of spatial econometrics. In case studies, we also found that the key role of innovation drive and social contribution in HQDCI has been highlighted, and the balance of development has constrained HQDCI in some regions. In terms of spatial analysis, we find that the role of economic circles and city clusters in promoting HQDCI deserves attention, mainly because economic circles and city clusters can drive regional coordination, resource integration, and innovation diffusion. This paper expects to provide some insights into the transformation and sustainable development of other developing countries through this evaluation and analysis system based on the transformation of China’s construction industry.

Keywords:

high-quality development; construction industry; catastrophe theory; spatial evolution analysis

1. Introduction

As a pillar of the national economy, the construction industry plays a crucial role in shaping a country’s development [1]. Its growth has therefore remained a key area of focus. Historically, the industry’s main emphasis was on the production of building materials [2]. However, with the rise in globalization, this focus has broadened beyond design and construction to cover the entire lifecycle of projects—from initial conception to eventual demolition—often described as “from cradle to grave.” Given its substantial contribution to national development, the construction industry also shoulders greater economic, environmental, and social responsibilities than other sectors [2,3]. In response, the international construction industry has revised its three core objectives, aligning them with the principles of sustainable development. The evolution of these objectives is depicted in Figure 1 [4].

In the global shift in the focus of the construction industry, the construction sectors in developing countries are generally lagging behind [2,5], which has led to slow progress in the industry’s sustainable development [6]. Moreover, both theoretical and practical research in this field remain limited, hindering the transformation of development strategies in these countries [7]. As the largest developing country, China has ranked first in the world in the construction market since 2010 [2]. However, as shown in Figure 2, the growth rate of China’s construction output has gradually slowed down over the past decade. Some scholars have pointed out that although China’s construction industry experiences cyclical fluctuations, these fluctuations have become increasingly stable. This stability is partly due to the government’s long-term planning and policy guidance for infrastructure investment, particularly under major policy frameworks like the “14th Five-Year Plan”, which has gradually shifted the industry away from over-reliance on cyclical investments [8]. On the other hand, it reflects the maturation of market demand, with construction companies adapting to the requirements of high-quality development, driving the industry toward green, smart, and internationalized directions [9]. However, behind this stable growth, there are still shortcomings in the industry’s technological innovation, resource efficiency, and environmental sustainability, creating an urgent need for new development paths to unleash greater growth potential and international competitiveness.

From the perspective of development philosophy, China’s high-quality development path in the construction industry plays a significant role in global trends and aligns closely with global themes such as sustainable development, technological innovation, and green transformation [10,11]. Especially under the guidance of the “14th Five-Year Plan”, China’s construction industry is transitioning from a traditional model based on scale expansion to a modern development phase that emphasizes quality improvement and structural optimization [8]. This transformation not only accelerates the digitalization, intelligence, and greening processes in the construction sector but also provides a practical example for the reform of the construction industry in developing countries through policy development, technological support, and international collaboration [3,12,13]. For instance, China’s vigorous promotion of prefabricated construction and smart building technologies has improved industry efficiency while also reducing resource consumption and environmental impacts [14,15]. This model offers valuable insights for other developing countries in balancing the demands of global climate change and economic development.

However, there are still several challenges in current research on the construction industry’s development, such as data scarcity and the difficulty of quantifying relevant indicators [16]. Although some scholars have developed index systems to assess the construction industry’s development status, these systems are still limited in terms of comprehensiveness and practicality [17]. In light of these challenges, this paper, based on the current state of China’s construction industry, develops an evaluation system for high-quality development and a spatial analysis framework. These tools aim to assess the high-quality development level in certain regions, providing a reference for evaluating the development of the construction industry in other countries or regions.

The sections of this article will include the following: In the second section, we will examine previous articles related to the development of the construction industry. The methodology will be elaborated in the third section. The fourth section will analyze and discuss a case study in the western region of China. The final section will provide a summary of the entire text. The specific research process is illustrated in Figure 3.

2. Literature Review

2.1. Research on the Influencing Factors of Construction Industry Development

Research on the development of the construction industry predominantly focuses on both internal and external factors influencing its progression. Early studies primarily concentrated on the internal dynamics of the industry itself. For instance, Arditi and Mochtar [18] employed a questionnaire survey to investigate the internal determinants of growth in the United States construction sector, concluding that factors such as quality, cost, schedule management, and labor efficiency have a substantial effect on the industry’s economic performance. Similarly, Dulaimi et al. [19] identified that professional standards, skill levels, constructability, safety protocols, and internationalization significantly influence the advancement of Singapore’s construction industry. Additionally, Crawford and Vogl [20] quantified construction productivity using two primary metrics: average labor productivity and total factor productivity. They noted that existing data systems exhibit certain limitations, advocating for the development of a robust quantitative framework to enhance subsequent research.

The integration of sustainable development principles has significantly transformed the paradigms of the international construction industry. Research has progressively shifted focus from traditional production factors to developmental factors influencing this sector. For instance, Rankin et al. [21] established a comprehensive set of indicators—including cost, time, scope, quality, safety, innovation, and sustainability—to assess the progress of the Canadian construction industry. Similarly, Naderpajouh et al. [22] introduced the Purdue Index for Construction (Pi-C) as a framework for evaluating industry development, developing a data-centric model that emphasizes data availability, usage frequency, and the dynamic interplay between data and industry practices, thereby fostering sustainable growth within the sector. Further advancements were made by Bhattacharyya et al. [23], who employed seasonal autoregressive integrated moving average, multiple linear regression, and random forest models to predict and analyze the Pi-C. Addressing gaps in development and quality metrics within the Pi-C, Jeon et al. [24] explored new indicators using the latent Dirichlet allocation method. Additionally, Blinn and Issa [25] highlighted the transformative impact of advanced building technologies on the Architecture, Engineering, Construction, and Operations (AECO) industry in the United States, emphasizing the potential of these technological advancements to enhance project quality and efficiency in various dimensions.

Examining external factors affecting the construction industry, Anaman and Osei-Amponsahn [26] conducted a Granger causality test on data from Ghana spanning 1968 to 2004. Their findings underscored the pivotal role of the construction sector as a key driver of economic growth in Ghana. Building on this, Ozkan et al. [27] investigated the interrelations among public construction investment, private construction investment, infrastructure investment, and GDP through comprehensive data analysis. They employed Engle–Granger cointegration, error correction modeling, and Granger causality tests to elucidate the causal relationships linking private construction investment and infrastructure investment to GDP.

Beyond primary economic considerations, the impact of environmental, social, and technological factors on the construction industry has increasingly garnered attention [28]. Sim and Putuhena [29] advocated for the adoption of a comprehensive development model to manage environmental concerns, positing that this approach could enhance the quality of development within Malaysia’s construction sector. Maskuriy et al. [30] emphasized the role of technological innovation in driving the Fourth Industrial Revolution—Industry 4.0—highlighting its spillover effects on the construction industry, which serve as a catalyst for enhancing development quality. Analyzing the dynamic and heterogeneous causal relationships among financial development, the construction industry, energy consumption, and environmental quality in China from 2001 to 2016, Ahmad et al. [31] employed various causal analysis methods, including the correlation benefit approach, and concluded that a bidirectional positive causal relationship exists between the construction industry and GDP. Furthermore, Kaklauskas et al. [32] noted that factors such as quality of life, macroeconomic conditions, human development, and social welfare significantly influence the sustainable development of the construction industry in EU member states, the UK, and Norway.

Historically, research on the construction industry has predominantly focused on internal influencing factors. However, in recent years, an increasing number of scholars have begun to examine the significant impact of external factors on the industry’s development [28]. Despite this growing recognition, external factors remain underrepresented in existing evaluation systems for the construction sector. As the industry matures, the influence of internal factors on its development has increasingly reached a saturation point, highlighting the critical importance of external factors. Therefore, it is essential to incorporate these external influences more thoroughly when constructing evaluation index systems for the construction industry.

2.2. Research on High-Quality Development of the Construction Industry in China

Research on China’s construction industry has highlighted its multifaceted impact on societal, economic, and environmental dimensions. Wang [6] analyzed these effects and concluded that a limited understanding of the social and environmental sustainability impacts hinders the sector’s sustainable development. Building on this, Wang et al. [33] identified ten key factors influencing the construction industry’s development and employed a structural equation model (SEM) for their analysis, revealing that asset levels and industry structure significantly affect industry growth. Ke [34] utilized a gray correlation model to investigate the relationship between scientific research investment and industry growth within China’s construction sector. His empirical findings indicated that from 2002 to 2012, the growth of research input was slow and insufficient to stimulate economic growth, highlighting a lack of sustainable momentum in the industry’s development. In a further advancement, Shen and Ren established an evaluation index for the coordinated development of the economy and ecological environment in China’s construction industry, incorporating five dimensions: scale expansion, sustainable growth, innovative development, green development, and open development. They employed a combined weighting model using the CRITIC-entropy weight method and the TOPSIS model for evaluation [35].

Moreover, research on the spatial evolution of the construction industry’s development remains limited, with most applications occurring in other fields. Liu et al. [36] employed a time series model to examine regional disparities and spatial convergence in the development potential of China’s construction industry. Additionally, they analyzed the spatial and temporal distribution of environmental impact efficiency within the sector, utilizing Moran’s index and spatial econometric statistics [37].

In summary, recent research on China’s construction industry tends to concentrate on one or several facets of sustainable development. However, such an approach captures only a partial view of the holistic quality of development in the construction industry (HQDCI), leaving many underlying dimensions inadequately addressed. A comprehensive evaluation must encompass all dimensions, considering every aspect of development quality within the sector. Furthermore, there is a notable lack of spatial analysis in this field, highlighting the need to examine regional variations in HQDCI more closely.

3. Methodology

3.1. High-Quality Development of the Construction Industry

3.1.1. Concept and Connotation

The concept of “high-quality development” was first introduced in China in 2017 [38] and has since gradually expanded from its initial economic focus to various industries. Given the relatively recent emergence of this concept, limited research has been conducted on HQDCI, and there is no consensus on its definition and connotation. Current interpretations of HQDCI are mainly framed in two ways: the first approach explores its meaning through the lens of economic development and high-quality economic growth, while the second focuses on defining HQDCI by incorporating the notion of quality with the specific characteristics of the construction industry.

Research following the first approach includes Yang et al. [39], who argued that the connotation of HQDCI should address social contradictions and facilitate the transformation and upgrading of the construction industry, emphasizing that its definition should encompass industry growth, development quality, and sustainability. Sun et al. [40] proposed that HQDCI can be understood in terms of both growth and quality, defining it as the ability or degree to which the construction industry meets user demands while aligning with the goals of sustainable economic, social, and environmental development.

Research from the second perspective focuses on redefining high-quality development in the construction industry. Wang and Li proposed a shift in focus from product-centric development to a customer-centered approach. They articulated the definition of high-quality development in both broad and narrow terms. Broadly, the connotation of HQDCI should integrate the new development paradigm, address key social contradictions, and account for the interconnected “society-culture-economy” relationship. In contrast, from a narrower perspective, HQDCI should encompass meeting evolving market demands, fostering innovative development methods and structures, and delivering higher levels of service to the public [41].

Based on the literature review, this paper defines HQDCI as the development of the construction industry guided by the new development paradigm, driven by scientific and technological innovation, and underpinned by environmental sustainability. The connotation of HQDCI encompasses the following key aspects:

(1): The scale of the industry is stable and making progress.

The impact of industry size on the development of the construction industry is widely acknowledged [13]. Achieving HQDCI requires, as a precondition, that the industry attains a certain scale. From a macroeconomic perspective, the construction industry serves as a cornerstone of the national economy. Economic growth drives the development of the construction sector, while the expansion of the construction industry, in turn, influences the broader economy, creating a dynamic interaction between the two. On a micro level, the stability of the industry’s size is closely linked to the performance of individual construction enterprises. The operations of these firms directly affect the overall scale and stability of the industry, while a stable industry environment supports the growth of construction enterprises. Thus, from the perspective of industry scale, HQDCI is reflected in three key dimensions: the size of construction enterprises, the scale of construction outputs, and the overall market size. Ensuring continuous growth in these areas is crucial for maintaining industry stability during periods of reform.

(2): The industry structure is reasonable.

Industry structure has long been a critical factor influencing the development of the construction sector [36,37]. A well-balanced industry structure contributes to overall stability and supports steady growth across the sector. Large construction enterprises, which generate a significant portion of industry output, play a pivotal role in shaping the trajectory of the entire industry. However, when numerous large firms lack the requisite strength, effective competition becomes difficult to sustain, hindering high-quality industry development. Only with a certain number of robust, competitive large enterprises can healthy competition flourish, driving enterprise growth and promoting the high-quality development of the construction industry. In addition, HQDCI also requires the presence of small, specialized enterprises. Past research has often overlooked the role of tertiary industry segments within construction, such as engineering consulting, supervision, and design services. With the growing emphasis on comprehensive project management, these tertiary enterprises are becoming increasingly critical in project execution. Therefore, from the perspective of industry structure, HQDCI is reflected in a high proportion of large, highly capable firms alongside the specialization of smaller enterprises.

(3): High output efficiency

Output efficiency is an important index to measure the efficiency of industry development [42,43]. High output efficiency can shorten the construction period, speed up the capital flow, and promote the development of the industry. Since HQDCI was proposed, most construction enterprises have gradually standardized the management of personnel. New technologies and new processes such as prefabricated construction are also being gradually implemented. All these have promoted the overall output efficiency of the construction industry, but there is still a certain gap between them and real high-quality development. High output efficiency not only ensures the stable development of the scale in the construction industry but also reflects the overall quality of the development of the construction industry. Therefore, from the perspective of output efficiency, HQDCI is mainly reflected in the following aspects: enterprises and individuals in the construction industry can use efficient management methods and technological means in the production process of construction products to improve productivity and yield.

(4): Driven by scientific and technological innovation

Scientific and technological innovation is a key strategic priority for China. In the construction industry, such innovation can be integrated at every stage of engineering projects to enhance efficiency, reduce costs, and optimize management systems, becoming a driving force for HQDCI. Under the influence of Industry 4.0, emerging technologies now play a crucial role throughout the building lifecycle [44]. Despite some advancements in integrating technological innovations across pre-construction, construction, and operational phases in China’s construction sector, challenges remain. These include an underdeveloped environment for innovation and difficulties in converting research into practical applications, contributing to a relatively low level of digitization that hampers HQDCI [45]. Only with sustained technological innovation can the construction industry ensure long-term growth and provide the necessary momentum for HQDCI. From this perspective, HQDCI is characterized by the industry’s substantial investment in research and development, high efficiency in translating scientific research into practical outcomes, and the effective application of technological advancements in both production and management processes.

(5): Energy conservation and emission reduction

In recent years, international scholars have increasingly recognized the significant impact of energy conservation and emission reduction on the development of the construction industry [46]. These efforts are also central to achieving the Sustainable Development Goals [47]. China, too, has emphasized the importance of harmonious coexistence between humans and nature, adhering to the development philosophy that “lucid waters and lush mountains are invaluable assets”. As a high-consumption and high-emission sector, the construction industry must be especially mindful of its ecological footprint. From an environmental perspective, HQDCI primarily involves two key areas. First, reducing resource consumption: adopting energy-efficient building products and promoting the recycling of construction materials can not only safeguard the natural environment but also lower costs and yield economic benefits. Second, reducing environmental pollution: the use of green materials and technologies can mitigate construction-related pollution, ensuring the construction industry’s capacity for sustainable development.

(6): High degree of contribution

HQDCI is not an isolated system but functions as a subsystem within the broader framework of high-quality development across all industries. This system must engage dynamically with the high-quality development of other sectors and contribute to the overall development of the economy. The exchange of technology and resources is essential for maintaining the stability of the system and ensuring its sustainable growth. For instance, economic progress stimulates the growth of the construction industry, while the construction sector’s output significantly contributes to national economic performance. The substantial output value generated by the construction industry can, in turn, drive further economic development. Moreover, the construction industry itself plays a key role in fostering interconnected growth. For example, when construction enterprises execute projects across regions, they often exchange technologies and management practices with local firms, fostering greater knowledge sharing and spurring regional development. Therefore, from a contribution perspective, HQDCI must also manifest in the high-quality development of other industries and the overall advancement of society.

3.1.2. Index System

The definition of connotation is only the theoretical basis of the evaluation system. In order to complete a specific evaluation, a set of usable index systems should be formed. Among the previous index system studies, the study of the Purdue Index for Construction (Pi-C) is systematic [22,23,25]. However, due to the accessibility of data, the universality of its application is limited. Therefore, this paper needs to consider the accessibility of data and the development of China to establish an evaluation index system.

Based on the six core aspects of HQDCI, this paper establishes six corresponding dimensions—industry scale, industry structure, output efficiency, innovation drive, energy conservation and emission reduction, and societal contribution—when constructing the HQDCI evaluation index system. The conceptual definitions of these dimensions are then translated into specific indicators, forming the HQDCI indicator system, as outlined in Table 1. A detailed description of each indicator is provided in Appendix A.1.

3.2. Catastrophe Progression Method

Catastrophe theory models changes in system states by constructing functional models [48], known as potential functions, to simulate these transitions. Each model requires a different potential function to accurately describe the system’s behavior. In the specific application of catastrophe theory, an evaluation model based on the catastrophe progression method should be established based on the evaluation index system for HQDCI.

3.2.1. Data Processing

Since there are great differences in the data units, numerical sizes, and positive and negative evaluation indexes (bigger is better or smaller is better), they are all involved in this paper. Therefore, it is necessary for the data to be normalized before being substituted into the model for calculation. The positive indicators (bigger is better) and the reverse indicators (smaller is better) need to be processed accordingly to make them become the same direction indicators. Generally, the range transformation method can be used to deal with such indexes. The final data obtained should be dimensionless, comparable values within the range of the value interval

[0, 1]

, so as to ensure the accuracy of subsequent calculations when substituted into the model.

For the data corresponding to the positive indicators, the formula below can be used for processing.

Due to significant differences in data units and numerical sizes, as well as the presence of both positive and negative evaluation indices (where some indicators are better when larger and others when smaller), normalization is essential before inputting data into the model. Positive indicators (higher values preferred) and reverse indicators (lower values preferred) must be adjusted to ensure consistency in direction. The range transformation method is typically used to achieve dimensionless, comparable values within the interval [0, 1], ensuring the accuracy of subsequent calculations.

For processing positive indicators, the following formula can be applied:

y_{j} = \frac{x_{j} - {x_{j}}^{m i n}}{{x_{j}}^{m a x} - {x_{j}}^{m i n}}

(1)

Data corresponding to reverse indicators can be processed according to the following formula:

y_{j} = \frac{{x_{j}}^{m a x} - x_{j}}{{x_{j}}^{m a x} - {x_{j}}^{m i n}}

(2)

In the formula:

x_{j}

is the original index data;

{x_{j}}^{m a x}

is the maximum value of indicator data;

{x_{j}}^{m i n}

is the minimum value of indicator data; and

y_{j}

is the index value after transformation.

3.2.2. Selection of Corresponding Model

After the original data of the underlying indicators are dimensionless, the mutation level value of each level of indicators is calculated according to the normalization formula of the selected mutation model. Then, the recursive calculation of each level of indicators is carried out layer by layer. Finally, the total mutation level of HQDCI can be obtained.

When choosing the normalization formula, it is necessary to follow the principle that the numbers of state variables and control variables match the corresponding normalization formula. However, the mutation model with 1 state variable and 6 control variables in this paper does not belong to the basic elementary mutation models mentioned above. According to the research, the recursive potential function of the basic elementary mutation model is as follows:

F (x) = x^{8} + a x^{6} + b x^{5} + c x^{4} + d x^{3} + e x^{2} + f x

(3)

Then, the normalization formula is derived. Finally, several corresponding methods involved in this paper are summarized as shown in Table 2.

Once the appropriate model is selected based on the number of control and state variables, it is essential to establish the specific calculation formula. This determination takes into account both the weight rankings of the indicators and any potential correlations between indicators within the same dimension.

In terms of weight ranking, this paper uses the expert survey method to determine it. This survey invited 5 experts from universities, 3 from enterprises, and 2 from relevant government departments to fill out a questionnaire. Since this survey is a small sample size and a highly targeted expert interview survey, there is no need to conduct reliability and validity tests. The weight sorting is shown in Table 3.

Regarding the correlation between indicators, the mutation order algorithm identifies two types of relationships at the same hierarchical level: “complementary” and “non-complementary”. A “complementary” relationship indicates a clear interdependence among control variables within the system, where a change in one indicator has a significant impact on others at the same level.

For such models, the “complementarity” principle should be used in the calculation, and the total mutation stage value is the average value after the normalized calculation of the mutation stage value at each layer. If there is a “non-complementary” relationship among the indicators at the same level, it means that the correlation between the control variables of the system is weak, and the change in one indicator will not have an impact on the other indicator or have a small impact. For such models, the “non-complementary” principle should be used in the calculation, and the total mutation stage value is the minimum value after the normalized calculation of the mutation stage value in each level. According to the overall structure and internal relationships among the evaluation index systems of HQDCI, the relationships between indicators of the same level are determined as shown in Table 4.

After the normalization formula, the index weight and corresponding relation principle are determined, all models are cascaded together to form an overall model, as shown in Figure 4.

In the calculation, the final mutation level value can be obtained by calculating from the bottom index layer by layer.

3.2.3. Interval Partition

To refine the highly aggregated results of the mutation grade model, traditional methods like average segmentation, quantile division, and uniform distribution are unsuitable for classifying the outcomes. Instead, the K-means clustering algorithm offers a more accurate approach. After selecting K cluster center points, each factor is grouped by calculating its distance to the nearest center. This iterative process continues until the termination condition is met. K-means clustering is unaffected by data aggregation levels and can better distinguish grade differences in the results, making it suitable for grading HQDCI evaluation outcomes.

Assuming HQDCI results are divided into two gradient categories, the criterion function is:

J = \sum_{j = 1}^{k} \sum_{i = 1}^{N_{j}} {‖X_{i} - Z_{j}‖}^{2}, X_{i} \in S_{j}

(4)

In the formula,

J

represents the square sum of the distance between the sample points of evaluation results and the center of the cluster.

S_{j}

represents the sample point set of each evaluation result;

Z_{j}

represents the center point of sample point set

S_{j}

of each evaluation result; and

N_{j}

represents the sample size of each evaluation result sample point set.

The algorithm aims to minimize the criterion function

J

, and the squared distance

J_{j}

between each evaluation sample point and its cluster center, solving the following equation:

\frac{\partial J_{j}}{\partial Z_{j}} = 0

(5)

Substitute Formula (4) into Formula (5) to obtain Formula (6):

\frac{\partial}{\partial Z_{j}} \sum_{i = 1}^{N_{j}} {‖X_{i} - Z_{i}‖}^{2} = \frac{\partial}{\partial Z_{j}} {(X_{i} - Z_{i})}^{T} (X_{i} - Z_{i}) = 0

(6)

By solving Formula (6), we can obtain the center point

Z_{j}

of sample point set

S_{j}

of evaluation results:

Z_{j} = \frac{1}{N_{j}} \sum_{i = 1}^{N_{j}} X_{i}, X_{i} \in S_{j}

(7)

Through the above iterative algorithm, the results of HQDCI can be divided into intervals, forming the development grade echelon.

3.3. Spatial Statistical Theory

3.3.1. Spatial Weight

This study conducts a spatial correlation analysis of HQDCI using ArcGIS 10.8 and GeoDa software V1.20. As the focus is on exploring the relationship between geographic spatial positions and HQDCI across provinces in western China, the model constructed in ArcGIS does not account for the precise spatial coordinates of each point. Therefore, considering the adjacency relationships between provinces, the Rook contiguity matrix is deemed most suitable for this analysis. The Rook matrix captures spatial distribution relationships by reflecting the adjacency between two factors, and the corresponding spatial weight matrix is determined based on the spatial adjacency function. The specific construction of the Rook contiguity matrix is as follows:

W = (\begin{matrix} \begin{matrix} ω_{11} & ω_{12} \end{matrix} & \dots & ω_{1 n} \\ \begin{matrix} ω_{21} & ω_{22} \end{matrix} & \dots & ω_{2 n} \\ \begin{matrix} \begin{matrix} ⋮ \\ ω_{n 1} \end{matrix} & \begin{matrix} ⋮ \\ ω_{n 2} \end{matrix} \end{matrix} & \begin{matrix} ⋮ \\ \dots \end{matrix} & \begin{matrix} ⋮ \\ ω_{n n} \end{matrix} \end{matrix}), ω_{i j} = \{\begin{matrix} 1, When two units are adjacent \\ 0, When two units are not adjacent \end{matrix}

(8)

3.3.2. Moran’s Index

After determining the spatial weight matrix, a spatial correlation analysis is conducted using the Moran’s Index, which is divided into two categories: global and local [49,50]. Moran’s I, ranging from −1 to +1, indicates positive correlation (>0), negative correlation (<0), or random distribution (=0) [51]. Global Moran’s I is often used to analyze whether there is a spatial correlation among elements. If there is a positive spatial correlation, it indicates that elements with a certain attribute are clustered. If the space is not correlated, it indicates that the elements with a certain attribute are randomly distributed. If there is a spatial negative correlation, it indicates that the elements with a certain attribute are dispersed. The model construction formula is as follows:

I = \frac{n \sum_{i = 1}^{n} \sum_{j = 1}^{n} w_{i, j} z_{i} z_{j}}{S_{0} \sum_{i = 1}^{n} {z_{i}}^{2}}

(9)

In the formula,

z_{i}

is the deviation between the attribute value of factor

i

and the mean value,

w_{i, j}

is the spatial weight of factors,

n

is the overall quantity of factors, and

S_{0}

is the set of all spatial weights.

Local Moran’s I is often used to determine whether the factor has a state of high value surrounded by low value, high value surrounded by high value, etc. Scatter plots or cluster plots are commonly used for further analysis. The model construction formula is as follows:

I_{n} = \frac{Z_{i}}{S^{2}} \sum_{j \neq i}^{n} w_{i j} Z_{j}

(10)

In the formula,

Z_{i} = y_{i} - \bar{y}

Z_{j} = y_{j} - \bar{y}

S^{2} = \frac{1}{n} \sum {(y_{i} - \bar{y})}^{2}

w_{i j}

is the spatial weight, and

n

is the overall quantity of factors.

4. Case Studies

4.1. Selection of Area and Time

This paper chooses western China as a case study. According to China’s economic and geographical divisions, western China consists of 12 regions, namely Chongqing Municipality, Sichuan Province, Shanxi Province, Yunnan Province, Guizhou Province, the Guangxi Zhuang Autonomous Region, Gansu Province, Qinghai Province, the Ningxia Hui Autonomous Region, the Tibet Autonomous Region, the Xinjiang Uygur Autonomous Region, and the Inner Mongolia Autonomous Region. According to the preliminary statistical data, the development of China’s construction industry is still unbalanced, and it is still strong in the east and weak in the west. However, the growth rate of the total output value of the construction industry in western China has been higher than that in eastern China, and the development potential of the construction industry in western China is huge. The total output value and growth rate of the construction industry in the eastern and western regions during 2015–2019 are shown in Figure 5 (in the eastern region, the total output value and growth rate are not included temporarily due to the absence of data in Hong Kong, Macao, and Taiwan).

In terms of time selection, considering that the development and change should be a process caused by qualitative changes, the curve of qualitative change should be relatively flat in the early stage of development, and it is difficult to select data from successive years to reflect the trend of change. Therefore, in this paper, the relevant data of interval years should be selected when selecting time-related data. Due to the lack of statistical data on relevant indicators in some regions in 2020 and 2021, this study cannot temporarily select relevant data from these two years as basic data. HQDCI was formally proposed in 2017. Based on the above restrictions, HQDCI in western China was evaluated based on the relevant basic data in 2015, 2017, and 2019. The study area is illustrated in Figure 6.

4.2. Data Source and Preprocessing

The original data in this paper are mainly collected from the National Bureau of Statistics, China Construction Industry Statistical Yearbook, China Science and Technology Statistical Yearbook, China Energy Statistical Yearbook, and other official channels, as well as the specific data sources outlined in Appendix A.5 (Appendix A).

Prior to conducting dimensionless processing, it is essential to assess whether the raw data can be used in its current form. To ensure data accuracy, it is important to minimize or eliminate any interference caused by regional or industrial variations. As a result, this study preprocesses the data by refining it from the overall industry down to the specific scope of the construction industry within each region. The dataset includes variables such as the number of R&D personnel in the construction sector, internal R&D expenditures, the number of patents granted, and total electricity consumption within the construction industry. The processing method is outlined in Formula (11):

y = \frac{x_{1} \cdot x_{2}}{x_{3}}

(11)

In Formula (11),

y

represents the construction industry data for a specific region, while

x_{1}

denotes the overall industry data for that region,

x_{2}

refers to the construction industry data across all regions, and

x_{3}

corresponds to the combined data of the overall industry and the construction industry across all regions.

The initial data formed after collecting and processing the data for 2015, 2017, and 2019 are shown in Appendix A.2, Appendix A.3 and Appendix A.4.

4.3. Evaluation Results

The catastrophe progression method was used to calculate the catastrophe progression value of the high-quality construction industry in provinces, municipalities, and autonomous regions of western China in 2015, 2017, and 2019, as shown in Figure 7.

From the perspective of dimensions, the scale, structure, output efficiency, innovation drive, energy conservation, emission reduction, and contribution to society of the construction industry in western China in 2015, 2017, and 2019 are shown in Figure 8.

After the evaluation is completed, the evaluation level should be divided. This part should be calculated according to the K-means clustering algorithm model mentioned above. This paper chooses SPSS Statistics 24 to finish that. The mutation level values of HQDCI in western China in 2015, 2017, and 2019 were taken as clustering factors and analyzed. Five clustering centers were set, and the maximum number of iterations was 500. If the variation range of each clustering center was less than 1 × 10⁻¹⁶, we think the convergence has been achieved. The results of the software operation iterations are shown in Table 5.

It can be seen from Table 5 that when iteration reaches the 21st time (not reaching the maximum number of iterations), each cluster center no longer changes or the changes are less than 1 × 10⁻¹⁶, indicating that the algorithm is over and the cluster center has been obtained. According to the clustering center, the evaluation grades of HQDCI in the western provinces, municipalities, and autonomous regions can be determined, as shown in Table 6 below.

4.4. Spatial Evolution Analysis

This paper uses ArcGIS and GeoDa software to conduct spatial analysis. Before the analysis, the evaluation data should be imported into the geographical layer of western China and the corresponding correlation, so as to build the GIS spatial model of HQDCI in the provinces, municipalities, and autonomous regions of western China in 2019.

4.4.1. Spatial Distribution of High-Quality Development Evaluations in Construction

After constructing the GIS spatial model of the evaluation of HQDCI in 2019 for the regions under the jurisdiction of provinces in western China, the factor division function in ArcGIS software is applied to import the grade division interval in the previous chapter into the software to form the grade distribution of HQDCI in western China in 2019 for provinces, autonomous regions, and municipalities directly under the Central Government, as shown in Figure 9.

As can be seen from Figure 9 the spatial distribution of the level of HQDCI in western China in 2019 shows a gradually decreasing trend from east to west. Provinces with a high level of HQDCI are mainly Shaanxi Province, Sichuan Province, and Chongqing Municipality, while Qinghai Province and the Tibet Autonomous Region have a low level of HQDCI. The main reasons for this spatial distribution are as follows: from the perspective of economic development, the economic strength of Sichuan Province, Chongqing Municipality, and Shaanxi Province takes the leading position in the whole western region, and their economic strength provides the necessary guarantee for HQDCI.

Geographically, the three regions are situated in the eastern part of the western area, serving as crucial hubs that connect the west with other regions. This positioning facilitates the absorption of advanced development concepts and technologies from the eastern and central regions. Consequently, the scientific and technological advancement in Chengdu, Chongqing, and Xi’an is accelerating, providing a vital impetus for HQDCI. Locally, these cities act as engines driving the opening and growth of the western region, having established a range of policies and initiatives aimed at promoting HQDCI. However, significant regional disparities in construction development persist across western China, resulting in an uneven landscape.

Following the preliminary spatial analysis of HQDCI, a further examination of the spatial distribution of specific aspects of HQDCI is warranted at the dimension evaluation level. ArcGIS software is utilized to categorize the industry scale, structure, output efficiency, innovation drive, energy conservation, emission reduction, and social contribution of HQDCI in the western region for 2019. This section focuses on comparing the index status levels within the same year; thus, the natural break point classification method, aligned with the principles of K-means clustering analysis, is employed in ArcGIS for grade classification. Figure 10 illustrates the spatial distribution of the industry scale, structure, output efficiency, innovation drive, energy conservation, emission reduction, and social contribution of the construction industry in western China for 2019.

In terms of spatial distribution across different dimensions, significant disparities exist. For instance, the industry scale and structure in western China show a declining gradient from east to west. However, the spatial patterns of output efficiency and energy conservation, as well as emission reduction, are less defined. This is because output efficiency is influenced not only by the capacity of construction enterprises but also by market saturation and regional economic conditions. Regarding energy conservation and emission reduction, varying energy consumption and waste emissions across different construction products lead to disparities in unit output value across regions. While Sichuan, Chongqing, and Shaanxi demonstrate relatively high levels of HQDCI, there are notable shortcomings. These regions exhibit no distinct advantages in output efficiency or sustainability metrics, and their progress has not significantly spurred development in neighboring provinces.

4.4.2. Spatial Correlation Analysis

The spatial analysis presented in the previous section provides a preliminary macro-level assessment based on direct observation, yet it lacks quantitative analysis using specific values and evaluation criteria. While this approach identifies spatial trends, it falls short in evaluating the aggregate and spillover effects across the region. Therefore, following the initial spatial analysis of HQDCI in western China, it is crucial to perform a more rigorous spatial correlation analysis. This should include both global and local spatial correlation assessments to offer a more comprehensive understanding of spatial interactions.

(1): Global spatial correlation

In this study, the spatial autocorrelation tool in ArcGIS 10.8 is used to calculate HQDCI in 2019 in each western region, and then the Moran’s I and various related indexes can be obtained.

Based on the Moran’s I evaluation criteria and the data in Figure 11, the Moran’s I for the global distribution of high-quality development of the construction industry (HQDCI) in western China in 2019 was 0.190, within the range of (0, 1), indicating a positive spatial correlation and potential spatial clustering. The p-value of 0.052 suggests that the probability of spatial data being randomly distributed is 5.2%, meaning that aggregation is more likely than random distribution, though the null hypothesis cannot be strongly rejected. The z-score of 1.941, while greater than 1.65, is less than 1.96, signifying a weak but statistically significant spatial correlation and some degree of clustering. Subsequently, the spatial autocorrelation tool in ArcGIS was applied to calculate Moran’s I and related indices for the various dimensions of HQDCI, including the industry scale, industry structure, output efficiency, innovation drive, energy conservation and emission reduction, and social contribution. The results are displayed in Figure 12.

As can be seen from Figure 10, in the dimension of HQDCI in western China, the dimension of industry scale shows a strong spatial aggregation phenomenon. The dimensions of innovation drive and contribution to society show weak spatial aggregation phenomena. The dimensions of industry structure, output efficiency, energy conservation, and emission reduction are randomly distributed. There are no significant spatially dispersed dimensions.

From an overall spatial correlation perspective, the development of construction industry quality in western China exhibits a degree of spatial clustering, though this effect remains moderate. Specifically, Sichuan, Chongqing, and Shaanxi show pronounced spatial spillover effects in terms of industry scale, innovation, and social contribution, driving development in neighboring regions. However, no significant spillover effect is observed in terms of industry structure.

Several factors contribute to this pattern. First, the regional development mechanism of the Chengdu-Chongqing Economic Circle plays a pivotal role in fostering a construction industry corridor between Sichuan and Chongqing. As China’s fourth-largest urban agglomeration, this economic circle has significantly advanced the economic development of both Sichuan and Chongqing since its establishment [52]. Its benefits extend beyond economics, encompassing improvements in ecology, healthcare, and education [53,54,55,56]. The relationship between the construction industries of these two regions has shifted from competition to cooperative growth, resulting in advantages such as enhanced business collaboration, technology exchange, and resource sharing. This collaboration has begun to extend its influence to areas surrounding the Chengdu-Chongqing Economic Circle, promoting high-quality construction development in these regions.

Secondly, due to rapid development and market saturation in Sichuan, Chongqing, and Shaanxi, construction enterprises in these areas are expanding into neighboring regions, thereby generating spillover effects. Lastly, the construction industry chain and systems in these provinces are more comprehensive than in other regions, enabling them to swiftly transition from high-speed to high-quality development modes.

(2): Local spatial correlation

In this study, the analysis of the local correlation of HQDCI is carried out by GeoDa software. The results of univariate spatial analysis in GeoDa are as follows:

The first quadrant of the four quadrants of the scatter diagram represents “high-high” (H-H) aggregation, indicating that the level of HQDCI in this part of the region and HQDCI in the surrounding area are high. The second quadrant represents the “low-high” (L-H) aggregation, indicating that HQDCI in this part of the region is at a low level, but HQDCI in the surrounding area is at a high level. The third quadrant represents the “low-low” (L-L) aggregation, indicating that HQDCI in this part of the region and HQDCI in the surrounding area are at a low level. The fourth quadrant represents the “high-low” (H-L) aggregation, indicating that HQDCI in this part of the region is at a higher level, but HQDCI in the surrounding area is at a lower level [57]. The critical point is divided into the quadrant where its deflection is the largest.

Figure 13 presents the local spatial correlation analysis of HQDCI in western China for 2019, while Figure 14 depicts the spatial correlation analysis of industry scale, structure, output efficiency, innovation drive, energy saving and emission reduction, and social contribution in the construction industry within the same region.

Since marking the names of each factor in the scatter diagram will confuse the picture layout, this paper organizes the corresponding information of the scatter diagram as shown in Table 7 below.

Although the scatter diagram effectively conveys local spatial correlations, some points are situated in critical locations where their correlations are weak. In the subsequent analysis, these points should not be classified within the four previously identified types of local spatial correlation. Therefore, local indicators of spatial association (LISA) cluster maps are utilized to further examine regions with significant local spatial correlations. The LISA significance and cluster maps illustrating the level of HQDCI in western China for 2019 were generated using GeoDa software, as shown in Figure 13.

Figure 15 illustrates that the local spatial correlation of HQDCI is particularly pronounced in the Xinjiang Uygur Autonomous Region, Guizhou Province, and Chongqing Municipality, while the other regions exhibit no significant correlations. Notably, Guizhou Province and Chongqing Municipality demonstrate “high-high” clustering with their neighboring areas, whereas the Xinjiang Uygur Autonomous Region shows a “high-low” relationship with its surroundings.

Subsequently, a detailed local spatial correlation analysis is conducted for various aspects of the construction industry in western China in 2019, including industry scale, industry structure, output efficiency, innovation drive, energy conservation, emission reduction, and societal contributions. The results are presented in the LISA significance map and LISA cluster map, as depicted in Figure 16 and Figure 17.

From the perspective of local spatial correlation, HQDCI exhibits a notable spatial aggregation effect in western China. Specifically, Chongqing Municipality and Guizhou Province, along with their surrounding areas, demonstrate a “high-high” aggregation effect, indicating that both these regions and their neighbors possess elevated levels of HQDCI, thereby fostering a significant link in their developmental trajectories. This linkage is particularly evident in dimensions such as industry scale, industry structure, and innovation drive.

Conversely, some regions display a “high-low” or “low-high” aggregation effect, characterized by weak sharing of HQDCI and limited connectivity with their surroundings. For instance, while the HQDCI level in the Xinjiang Uygur Autonomous Region is relatively strong, the development in neighboring areas remains underwhelming, failing to capitalize on its potential as a driving force. Although Xinjiang’s HQDCI is high compared to its immediate surroundings, it ranks only at a medium level within the broader context of western China, indicating that its intrinsic strengths are insufficient to stimulate the development of adjacent regions.

5. Discussion

Sichuan Province, Shaanxi Province, and Chongqing are in the first echelon of HQDCI in western China. The reason is that these three regions belong to the core provincial-level units in western China, which have higher levels of policy promotion, economy, and science and technology, and have absolute advantages compared with other western regions [58]. It provides a solid foundation and an important guarantee for HQDCI. Especially in the aspect of science and technology, Sichuan Province has rich innovation resources and policy support and is gradually building itself into a highland of science and technology innovation and development in western China [59]. Also in the construction industry, Sichuan Province has significant advantages in scientific research input, staffing of researchers, and output of research results, which enables it to quickly adapt to the transformation of the development mode in the construction industry [14]. From the dimensional indicators, these three regions have no obvious shortcomings or fewer shortcomings in HQDCI, and the development in all aspects is more balanced compared with other regions [17]. Among them, the level of industry scale, innovation drive, and contribution to society are significantly higher than other units in the western region. From the secondary indicators, although some indicators have a lower development level, most of them are still in the leading areas. In particular, the output value of the construction industry, the amount of contracts signed, scientific research personnel, scientific research investment, energy consumption, and output value completed outside the province are significantly better than other units in the western region [60].

Yunnan Province, the Guangxi Zhuang Autonomous Region, Guizhou Province, Gansu Province, and the Xinjiang Uygur Autonomous Region are in the second and third gradients. Compared with the first gradient, these two gradient areas are in two situations. One is that most of the indicators are relatively more average; the development is balanced, but the overall development level is lower than the first gradient areas; the second is that there are both high-level indicators and low-level indicators, and the development is unbalanced. In terms of dimensional indicators, the Guangxi Zhuang Autonomous Region, the Xinjiang Uygur Autonomous Region, and Yunnan Province have relatively average development levels of overall dimensional indicators, most of which are in the midstream level [61]. Gansu and Guizhou provinces, on the other hand, have unbalanced development, with Gansu Province having a higher level of energy conservation, energy saving, and emission reduction, and the development level of other dimensional indicators being at the middle and lower level [13,60], and Guizhou Province having a higher level of output efficiency and the development level of other dimensional indicators being at the middle level [62]. In terms of the secondary indicators, most of the secondary indicators in the regions under these two gradients fluctuate in the middle and lower levels, but some indicators with low development levels constrain the overall level [13].

HQDCI in western China is in the fourth and fifth gradient, including the Inner Mongolia Autonomous Region, the Ningxia Hui Autonomous Region, Qinghai Province, and the Tibet Autonomous Region [63]. Compared with other gradient regions, there are also two situations: first, most indicators are at a low level; second, most indicators are at a low level. But there are individual high-level indicators and low-level indicators. In terms of dimensional indexes, most indexes of Qinghai Province, the Inner Mongolia Autonomous Region, and the Ningxia Hui Autonomous Region are at the lower or middle-lower level. The output efficiency, energy savings, and emission reduction in the Tibet Autonomous Region are at a high level, while other indicators are at a low level [63]. From the perspective of secondary indicators, most of the indicators under the two gradient units are in the middle and lower reaches or at a low level, and only a few indicators are at a high level [64]. For example, the per capita profit, capital profit rate, energy consumption, and construction material consumption of the Tibet Autonomous Region are at a high level, but the other indicators are at a low level and the development is unbalanced [65], which has restricted HQDCI in the Tibet Autonomous Region.

It is not difficult to see from the above results that the current high-quality development model of China’s construction industry has been significantly different from the previous high-speed development model, which often takes the industry scale as the main benchmark, that is, the total output value of construction enterprises, the number of enterprises, the total profit, and so on [66]. Although the sustainable development of the construction industry has been considered in the following years, it still has less considered the innovation drive and social contribution [13,67,68]. However, through the results of this evaluation, we can observe that the regions in the first echelon of development have obvious advantages in terms of industry scale, scientific and technological innovation, energy consumption, and social contribution, and the overall balance of development is stronger than in other echelons [69]. Therefore, it is confirmed that in the process of HQDCI, although the industry scale still needs to maintain growth, scientific and technological innovation, social contribution, and energy consumption have begun to play an important role in HQDCI [70]. In addition to the situation of the first echelon, the least developed echelon also reflects that only a few aspects of high-level development cannot achieve high-quality development, which once again shows that comprehensive and balanced development is crucial in HQDCI.

6. Conclusions

This study delineates the concept and implications of HQDCI and establishes an evaluation index system alongside a spatial analysis framework grounded in catastrophe theory and relevant spatial econometrics. This approach aims to provide a valuable reference for assessing the development and transformation of the construction industry in developing countries. Ultimately, this paper focuses on the construction industry in western China as a case study, evaluating the progress of HQDCI and analyzing its spatial evolution. The key conclusions drawn from this research are as follows:

(1): Evaluation system: Based on the definition of HQDCI and literature research, this paper constructs an index system of HQDCI from six aspects: industry scale, industry structure, output efficiency, innovation drive, energy conservation and emission reduction, and contribution to society. In addition, according to the characteristics of HQDCI, such as hysteresis and mutability, with the catastrophe progression method as the main theoretical method, combined with K-means clustering, etc., the evaluation model of HQDCI is established.
(2): Spatial analysis: Based on the layer theory and spatial correlation theory, this paper builds a spatial analysis model for HQDCI. After the evaluation of HQDCI, it can analyze the spatial analysis from the global and local perspectives to explore whether there is a linkage development mechanism between regions and whether there is a negative effect.
(3): Case analysis: This paper uses the evaluation and analysis model of HQDCI and makes evaluation and spatial evolution analysis of 12 provincial units in western China based on the yearbook data. The study found that in western China, Sichuan Province, Shaanxi Province, and Chongqing Municipality had a high level of HQDCI; Yunnan Province, the Guangxi Zhuang Autonomous Region, Guizhou Province, Gansu Province, and the Xinjiang Uygur Autonomous Region had a general level of HQDCI; and the Inner Mongolia Autonomous Region, the Ningxia Hui Autonomous Region, Qinghai Province, and the Tibet Autonomous Region had a low level of HQDCI. In addition, from the perspective of the dimensions of the evaluation results, in the process of HQDCI, industry scale, energy saving, and emission reduction still need to be paid attention to, and innovation drive and contribution to society have become the key elements of industry quality development. The overall balance of development should also be paid attention to.

In spatial evolution, HQDCI in western China has a certain spatial aggregation effect, but the aggregation effect is not strong. Only Sichuan Province, Chongqing Municipality, and Shaanxi Province have a relatively obvious spatial spillover effect on the surrounding areas in the dimensions of industry scale, innovation drive, and contribution to society. In this regard, the effect of the Chengdu-Chongqing economic circle on the high quality of the construction industry in Sichuan and Chongqing should be paid attention to. Properly playing the role of the economic circle and urban agglomeration will further extend the spatial spillover effect and realize HQDCI in a wider range.

Finally, it is worth noting that the evaluation system should be continuously optimized. Although this paper constructs an evaluation system of HQDCI that includes a complete set of evaluation index systems, an evaluation model, and a spatial analysis model, due to the short time since the concept of HQDCI was proposed, the mutability of the quality development curve has not been reflected in this study. Meanwhile, if there are more relevant data or quantitative means in the future, further optimization of the index system can be considered. In addition, the spatial analysis in this paper is still a primary correlation test, which can be further studied by using spatial econometric statistical means in the future.

Author Contributions

Conceptualization, Y.X. and H.Y.; methodology, Y.X. and H.Y.; software, Y.S. and H.Y.; validation, Y.X., H.Y. and Y.W.; formal analysis, H.Y.; investigation, Y.X.; resources, Y.X.; data curation, Y.S. and H.Y.; writing—original draft preparation, Y.X., H.Y., Y.W. and Y.S.; writing—review and editing, Y.X. and H.Y.; visualization, H.Y.; supervision, Y.X.; project administration, Y.X.; funding acquisition, Y.X. and H.Y. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

All data is provided in the Appendix A.

Conflicts of Interest

The authors declare no conflict of interest.

Appendix A

Appendix A.1. Initial Indicator Information

Indicators	Unit	Description of Indicators	Types of Indicators
Output value of construction	10 thousand CNY	It refers to the sum of construction products and services produced by each enterprise in the construction industry in a certain period, expressed in monetary terms each year.	positive
Total value of contracts by construction enterprises	10 thousand CNY	It refers to the annual number of contracts expressed in monetary terms for the construction industry products produced by each enterprise in the construction industry during a certain period.	positive
Asset-liability ratio	%	This refers to the percentage of total liabilities divided by total assets at the end of each year for construction companies (this paper classifies it as an inverse indicator from the perspective of corporate risk resistance).	reverse
Number of construction enterprises	unit	It refers to the annual number of legal entities engaged in the construction of buildings, structures, and equipment installation activities.	positive
Structure of enterprise qualification	%	It refers to the ratio of the number of general contracting construction enterprises with special grade qualification to the total number of construction enterprises each year (this paper takes into account that for construction enterprises at this stage, the larger the number of units of general contracting construction enterprises with special grade qualification, the more beneficial to the development of the industry).	positive
Structure of enterprise type	%	It refers to the ratio of the number of survey and design institutions to the total number of construction enterprises and the ratio of the number of construction supervision enterprises to the total number of construction enterprises each year (this paper takes into account that for construction enterprises at this stage, the larger the number of survey and design institutions and the number of construction supervision enterprises, the more beneficial to the development of the industry). The specific calculation when dimensionless processing is the average value of the two types of calculated values.	positive
Capital profit margin	%	It refers to the annual total profit of each enterprise in the construction industry as a percentage of the total capital (paid-in capital, registered capital).	positive
Per capita profit	CNY/person	It refers to the annual construction industry enterprises in the production and operation process of various incomes after deducting all kinds of consumption surplus.	positive
Labor productivity	CNY/person	It refers to the labor productivity of the construction industry enterprises calculated by the total construction output value each year.	positive
Equipment condition	kW/person	It refers to the ratio of the net value of owned machinery and equipment of each enterprise in the construction industry to the number of all employees at the end of each year and the ratio of the total power of owned machinery and equipment of enterprises in the construction industry to the number of all employees at the end of the year; the specific calculation when dimensionless processing is the average value of the two types of calculated values.	positive
Number of researchers	person	It refers to the number of people engaged in research and experimental development (R&D) from the construction industry each year.	positive
R&D investments	10 thousand CNY	It refers to the annual construction industry research and experimental development (R&D) funding internal expenditure amount.	positive
Scientific payoffs	unit	It refers to the number of patent applications filed by companies in the construction industry each year.	positive
Energy consumption	kWh/10 thousand CNY	It refers to the sum of annual electricity consumption per unit of output in the construction industry.	reverse
Construction material consumption	Steel, cement: ton/thousand CNY; wood, glass: m³/thousand CNY; aluminum: ton/hundred CNY	It refers to the annual steel consumption per unit of output value in the production of construction products, wood consumption per unit of output value in the production of construction products, cement consumption per unit of output value in the production of construction products, glass consumption per unit of output value in the production of construction products, and aluminum consumption per unit of output value in the production of construction products; the specific calculation when dimensionless processing is the average value of the five types of calculated values.	reverse
Increase in tax revenue	10 thousand CNY	It refers to the total amount of taxes paid by each enterprise in the construction industry as required each year.	positive
Output value outside the province	10 thousand CNY	It refers to the total output value of each enterprise in the construction industry completed outside the province each year.	positive

Appendix A.2. The Original Data of High-Quality Development of the Construction Industry in Western China in 2015

Name of Data	Units of Data	Raw Data
Name of Data	Units of Data	National	Chongqing	Sichuan	Guizhou	Yunnan	Tibet	Shaanxi	Gansu	Qinghai	Ningxia	Xinjiang	Guangxi	Inner Mongolia
Gross output value of construction	100 billion CNY		6.257	8.768	1.948	3.269	0.107	4.753	1.849	0.410	0.525	2.256	2.953	1.123
The number of contracts signed by construction companies	trillion CNY		0.986	1.781	0.511	0.594	0.015	0.997	0.298	0.091	0.081	0.363	0.564	0.198
Gearing ratio of enterprises in the construction industry	%		70.5	70.5	73.4	67.0	49.5	68.2	67.0	66.2	70.5	75.5	66.3	64.4
Number of construction enterprises	unit		2492	3449	742	2417	167	1878	1264	366	503	1114	1071	841
Number of general contracting construction enterprises with special grade qualification	unit		2	12	2	3	1	6	5	1	1	4	3	1
Number of prospecting and designing institutions	unit		464	490	244	702	43	633	292	140	101	348	390	282
Number of construction project supervision enterprises	unit		99	362	113	146	3	438	171	64	61	98	163	167
Capital margin	%		40.0	17.5	17.5	18.6	17.8	14.5	17.9	12.7	14.3	14.7	15.2	13.9
Profit per capita	thousand CNY/person		1.535	0.747	0.819	1.088	2.261	1.010	1.020	1.107	0.909	0.606	0.551	1.149
Labor productivity based on total construction industry output	100 thousand CNY/person		3.128	2.958	3.497	2.878	3.083	3.731	3.036	3.199	2.669	2.841	2.994	2.768
Technical equipment rate	100 thousand CNY/person		0.692	1.166	0.792	1.313	1.837	1.655	1.348	2.035	1.113	1.163	0.605	2.373
Power equipment rate	kW/person		2.2	3.7	3.9	5.3	7.3	6.0	6.5	8.9	4.8	6.2	2.9	6.4
Number of R&D personnel	100 thousand persons	54.825	0.978	1.987	0.405	0.675	0.021	1.325	0.408	0.067	0.161	0.308	0.648	0.507
Number of R&D personnel in the construction industry	person	12298	219	446	91	152	5	297	91	15	36	69	145	114
Intramural expenditure on R&D	10 billion CNY	141.699	2.470	5.029	0.623	1.094	0.031	3.932	0.827	0.116	0.255	0.520	1.059	1.361
Intramural expenditure on R&D in the construction industry	million CNY	320.229	5.582	11.365	1.408	2.471	0.071	8.885	1.869	0.262	0.576	1.175	2.394	3.075
Number of patents for industrial enterprises	10 thousand units	63.851	2.024	2.191	0.378	0.375	0.002	0.752	0.223	0.031	0.143	0.234	0.461	0.259
Number of patents for construction companies	Unit	1728	55	59	10	10	0	20	6	1	4	6	12	7
Consumption of electricity	100 billion kWh	58.020	0.875	1.992	1.174	1.439	0.041	1.222	1.099	0.658	0.878	2.160	1.334	2.543
Consumption of electricity in the construction industry	Billion kWh	698.7	10.5	24.0	14.1	17.3	0.5	14.7	13.2	7.9	10.6	26.0	16.1	30.6
Electricity consumption per unit of output	kWh/10 thousand CNY		16.8	27.4	72.6	53.0	46.2	31.0	71.6	193.5	201.6	115.3	54.4	272.6
Steel consumption in the construction industry	10 million tons		1.798	4.152	2.282	1.100	0.038	1.952	0.701	0.085	0.251	0.528	0.840	0.548
Steel consumption per unit of output	ton/thousand CNY		2.9	4.7	11.7	3.4	3.5	4.1	3.8	2.1	4.8	2.3	2.8	4.9
Wood consumption in the construction industry	10 million m³		0.808	2.357	0.676	0.623	0.017	1.071	0.578	0.033	0.206	0.243	2.247	0.286
Wood consumption per unit of output	m³/thousand CNY		1.3	2.7	3.5	1.9	1.6	2.3	3.1	0.8	3.9	1.1	7.6	2.5
Cement consumption in the construction industry	10 million tons		5.783	10.593	4.011	2.982	0.081	5.425	1.861	0.337	0.589	1.748	3.240	1.548

Appendix A.3. The Original Data of High-Quality Development of the Construction Industry in Western China in 2017

Name of Data	Units of Data	Raw Data
Name of Data	Units of Data	National	Chongqing	Sichuan	Guizhou	Yunnan	Tibet	Shaanxi	Gansu	Qinghai	Ningxia	Xinjiang	Guangxi	Inner Mongolia
Gross output value of construction	100 billion CNY		7.606	11.400	2.933	4.726	0.148	6.227	1.825	0.407	0.549	2.419	4.210	1.122
The number of contracts signed by construction companies	trillion CNY		1.234	2.363	0.788	1.029	0.024	1.401	0.351	0.095	0.095	0.453	0.821	0.223
Gearing ratio of enterprises in the construction industry	%		69.7	59.5	74.3	67.7	57.0	72.3	69.7	68.8	68.0	77.3	64.4	65.9
Number of construction enterprises	unit		2707	4501	1029	2656	231	2388	1363	364	681	1157	1235	886
Number of general contracting construction enterprises with special grade qualification	unit		4	36	10	4	14	10	5	1	1	3	6	1
Number of prospecting and designing institutions	unit		503	1364	301	728	79	838	309	154	109	364	769	364
Number of construction project supervision enterprises	unit		107	348	148	172	43	474	189	65	63	119	182	158
Capital margin	%		39.7	8.1	21.9	16.4	34.3	14.9	14.6	8.4	12.4	16.3	16.4	13.8
Profit per capita	thousand CNY/person		1.420	0.706	1.262	1.082	4.245	1.121	0.978	0.757	0.901	0.789	0.544	1.456
Labor productivity based on total construction industry output	100 thousand CNY/person		3.174	2.885	3.663	3.136	3.122	4.082	2.930	2.846	2.430	3.096	3.021	3.127
Technical equipment rate	100 thousand CNY/person		0.529	0.810	0.940	0.853	1.693	1.110	1.262	2.963	0.944	1.063	0.504	1.827
Power equipment rate	kW/person		2.1	3.3	5.0	3.4	5.6	4.7	6.6	10.1	5.9	6.0	2.4	6.7
Number of R&D personnel	100 thousand persons	62.136	1.320	2.416	0.527	0.776	0.025	1.508	0.410	0.097	0.172	0.288	0.720	0.488
Number of R&D personnel in the construction industry	person	14492	308	563	123	181	6	352	96	23	40	67	168	114
Intramural expenditure on R&D	10 billion CNY	176.061	3.646	6.378	0.959	1.578	0.029	4.609	0.884	0.179	0.389	0.570	1.422	1.323
Intramural expenditure on R&D in the construction industry	million CNY	416.778	8.632	15.099	2.270	3.735	0.068	10.911	2.093	0.424	0.922	1.348	3.366	3.133
Number of patents for industrial enterprises	10 thousand units	81.704	1.727	2.669	0.534	0.539	0.002	0.923	0.310	0.073	0.198	0.302	0.543	0.380
Number of patents for construction companies	unit	2641	56	86	17	17	0	30	10	2	6	10	18	12
Consumption of electricity	100 billion kWh	65.914	0.997	2.205	1.385	1.538	0.058	1.495	1.164	0.687	0.978	2.543	1.445	2.892
Consumption of electricity in the construction industry	Billion kWh	789.22	11.9	26.4	16.6	18.4	0.7	17.9	13.9	8.2	11.7	30.4	17.3	34.6
Electricity consumption per unit of output	kWh/10 thousand CNY		15.7	23.2	56.5	39.0	47.1	28.7	76.4	202.1	213.3	125.9	41.1	308.6
Steel consumption in the construction industry	10 million tons		2.379	5.570	1.145	1.456	0.031	2.387	0.603	0.114	0.123	0.512	1.759	0.402
Steel consumption per unit of output	ton/thousand CNY		3.1	4.9	3.9	3.1	2.1	3.8	3.3	2.8	2.2	2.1	4.2	3.6
Wood consumption in the construction industry	10 million m³		1.572	3.289	0.691	1.429	0.027	1.253	0.204	0.025	0.108	0.215	2.055	0.249
Wood consumption per unit of output	m³/thousand CNY		2.1	2.9	2.4	3.0	1.9	2.0	1.1	0.6	2.0	0.9	4.9	2.2
Cement consumption in the construction industry	10 million tons		6.270	15.256	0.346	3.973	0.108	6.136	1.727	0.436	0.567	1.519	4.472	2.583
Cement consumption per unit of output value	ton/thousand CNY		8.2	13.4	1.2	8.4	7.3	9.9	9.5	10.7	10.3	6.3	10.6	23.0
Glass consumption in the construction industry	10 million m³		2.219	6.517	2.381	1.251	0.021	3.274	0.662	0.209	0.167	0.981	2.059	0.552
Glass consumption per unit of output	m³/thousand CNY		2.9	5.7	8.1	2.6	1.4	5.3	3.6	5.1	3.0	4.1	4.9	4.9
Aluminum consumption in the construction industry	millions of tons		1.874	6.124	1.045	0.386	0.057	1.269	0.233	0.091	0.123	0.089	2.276	0.294
Aluminum consumption per unit of output	ton/hundred CNY		2.5	5.4	3.6	0.8	3.9	2.0	1.3	2.2	2.2	0.4	5.4	2.6
Total construction industry taxes	ten billion USD		2.537	3.183	0.863	1.592	0.057	1.711	0.682	0.141	0.196	0.830	1.038	0.425
Output value of construction completed in foreign provinces	100 billion CNY		1.246	2.303	0.686	0.367	0.001	1.924	0.318	0.156	0.044	0.148	0.676	0.102

Appendix A.4. The Original Data of High-Quality Development of the Construction Industry in Western China in 2019

Name of Data	Units of Data	Raw Data
Name of Data	Units of Data	National	Chongqing	Sichuan	Guizhou	Yunnan	Tibet	Shaanxi	Gansu	Qinghai	Ningxia	Xinjiang	Guangxi	Inner Mongolia
Gross output value of construction	100 billion CNY		8.223	14.668	3.715	6.122	0.220	7.884	1.916	0.461	0.601	2.278	5.407	1.086
The number of contracts signed by construction companies	trillion CNY		1.474	3.238	1.003	1.320	0.049	1.797	0.453	0.126	0.094	0.496	1.082	0.295
Gearing ratio of enterprises in the construction industry	%		71.2	70.5	74.1	67.8	62.5	75.2	68.2	68.8	69.6	75.1	67.7	68.8
Number of construction enterprises	unit		2939	5826	1449	3156	278	3067	1654	389	662	1320	1630	1026
Number of general contracting construction enterprises with special grade qualification	unit		6	31	11	9	1	31	5	1	1	6	12	4
Number of prospecting and designing institutions	unit		515	1151	303	763	53	1032	262	144	123	313	551	347
Number of construction project supervision enterprises	unit		130	466	175	170	47	386	201	77	67	144	222	140
Capital margin	%		32.5	16.9	16.4	20.8	39.7	9.9	14.5	4.8	9.2	9.8	15.4	7.9
Profit per capita	thousand CNY/person		1.284	0.960	1.162	1.404	4.444	1.097	1.309	0.680	0.743	0.726	0.719	1.205
Labor productivity based on total construction industry output	100 thousand CNY/person		3.483	3.520	4.089	3.375	3.282	4.608	3.539	4.391	2.939	3.646	3.786	4.063
Technical equipment rate	100 thousand CNY/person		0.438	0.726	0.732	0.790	0.673	1.005	1.192	2.057	0.744	1.052	0.372	2.130
Power equipment rate	kW/person		2.0	2.7	4.0	3.0	4.5	5.3	6.4	10.6	4.5	12.0	2.4	7.8
Number of R&D personnel	100 thousand person	71.293	1.607	2.701	0.673	0.930	0.029	1.676	0.460	0.097	0.209	0.256	0.824	0.399
Number of R&D personnel in the construction industry	person	18813	424	713	178	245	8	442	122	25	55	68	218	105
Intramural expenditure on R&D	10 billion CNY	221.436	4.696	8.710	1.447	2.200	0.043	5.846	1.102	0.206	0.545	0.641	1.671	1.478
Intramural expenditure on R&D in the construction industry	million CNY	594.868	12.615	23.397	3.887	5.911	0.116	15.704	2.962	0.553	1.464	1.722	4.490	3.971
Number of patents for industrial enterprises	10 thousand units	105.981	1.665	2.968	0.692	0.761	0.005	1.280	0.339	0.109	0.289	0.363	0.637	0.506
Number of patents for construction companies	unit	3803	60	106	25	27	0	46	12	4	10	13	23	18
Consumption of electricity	100 billion kWh	74.866	1.160	2.636	1.541	1.812	0.078	1.912	1.288	0.716	1.084	2.868	1.907	3.653
Consumption of electricity in the construction industry	Billion kWh	991.19	15.4	34.9	20.4	24.0	1.0	25.3	17.1	9.5	14.4	38.0	25.2	48.4
Electricity consumption per unit of output	kWh/10 thousand CNY		18.7	23.8	54.9	39.2	46.9	32.1	89.0	205.8	238.6	166.7	46.7	445.3
Steel consumption in the construction industry	10 million tons		2.514	9.770	1.818	2.685	0.038	3.031	0.744	0.190	1.214	1.053	1.560	0.370
Steel consumption per unit of output	ton/thousand CNY		3.1	6.7	4.9	4.4	1.7	3.8	3.9	4.1	20.2	4.6	2.9	3.4
Wood consumption in the construction industry	10 million m³		1.489	4.224	0.646	1.235	0.025	1.021	0.235	0.040	0.220	0.432	1.521	0.173
Wood consumption per unit of output	m³/thousand CNY		1.8	2.9	1.7	2.0	1.1	1.3	1.2	0.9	3.7	1.9	2.8	1.6
Cement consumption in the construction industry	10 million tons		6.436	18.704	4.399	4.604	0.093	7.845	1.422	0.342	0.882	1.241	4.467	0.724
Cement consumption per unit of output value	ton/thousand CNY		7.8	12.8	11.8	7.5	4.2	10.0	7.4	7.4	14.7	5.4	8.3	6.7
Glass consumption in the construction industry	10 million m³		2.213	6.293	1.738	1.274	0.023	2.295	0.483	0.135	0.116	0.486	2.031	0.325
Glass consumption per unit of output	m³/thousand CNY		2.7	4.3	4.7	2.1	1.1	2.9	2.5	2.9	1.9	2.1	3.8	3.0
Aluminum consumption in the construction industry	millions of tons		1.926	8.821	1.477	0.535	0.022	2.804	0.240	0.060	0.256	0.075	2.132	0.268
Aluminum consumption per unit of output	ton/hundred CNY		2.3	6.0	4.0	0.9	1.0	3.6	1.3	1.3	4.3	0.3	3.9	2.5
Total construction industry taxes	ten billion USD		2.570	3.988	0.862	1.885	0.093	1.899	0.712	0.131	0.184	0.701	1.326	0.429
Output value of construction completed in foreign provinces	100 billion CNY		1.470	2.937	0.898	0.528	0.008	2.539	0.297	0.186	0.065	0.342	0.852	0.187

Appendix A.5. Data Source

Data Source	URL
China Statistical Yearbook	https://www.stats.gov.cn/sj/ndsj/
Sichuan Statistical Yearbook	https://tjj.sc.gov.cn/
Chongqing Statistical Yearbook	https://tjj.cq.gov.cn/zwgk_233/tjnj/
Guizhou Statistical Yearbook	https://www.guizhou.gov.cn/
Yunnan Statistical Yearbook	https://stats.yn.gov.cn/List22.aspx
Shanxi Statistical Yearbook	http://tjj.shaanxi.gov.cn/
Ningxia Statistical Yearbook	https://www.nx.gov.cn/zwgk/zfxxgk/fdzdgknr/tjxx_40901/tjnj/
Qinghai Statistical Yearbook	http://tjj.qinghai.gov.cn/tjData/qhtjnj/
Inner Mongolia Statistical Yearbook	https://www.nmg.gov.cn/tjsj/sjfb/tjsj/tjgb/
Xinjiang Statistical Yearbook	https://tjj.xinjiang.gov.cn/tjj/zhhvgh/list_nj1.shtml
Gansu Statistical Yearbook	https://tjj.gansu.gov.cn/tjj/c109464/info_disp.shtml
Guangxi Statistical Yearbook	http://tjj.gxzf.gov.cn/tjsj/tjnj/
Links accessed on 17 September 2024.

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Figure 1. Trends in the three major objectives of the construction industry [4].

Figure 2. Growth rate and total output value of China’s construction industry from 2011 to 2020. Note: Data sourced from the National Bureau of Statistics of China: https://www.stats.gov.cn/sj/ndsj/ (accessed on 12 April 2024).

Figure 3. Research flowchart.

Figure 4. Evaluation model of HQDCI based on catastrophe theory.

Figure 5. The total output value and growth rate of the construction industry in eastern and western regions of China from 2015 to 2019. Note: Data sourced from the National Bureau of Statistics of China: https://www.stats.gov.cn/sj/ndsj/ (accessed on 12 July 2024).

Figure 6. Study area.

Figure 7. HQDCI in western China in 2015, 2017, and 2019.

Figure 8. Evaluation results for each dimension of HQDCI.

Figure 9. The spatial distribution of the level of HQDCI in western China. Note: This map contains only the 12 regions of western China relevant to the research and is not a complete map of China.

Figure 10. The spatial distribution of various dimensional levels of HQDCI. Note: This map contains only the 12 regions of western China relevant to the research and is not a complete map of China.

Figure 11. Moran’s I for the overall goal of HQDCI.

Figure 12. Moran’s I for various dimensional indicators of HQDCI.

Figure 13. Scatter plot frame of HQDCI in western China.

Figure 14. Scatter plot frame of dimensional indicators of HQDCI in western China.

Figure 15. The results of LISA significance and cluster analysis of HQDCI in Western China in 2019. Note: This map contains only the 12 regions of western China relevant to the research and is not a complete map of China.

Figure 16. The results of LISA significance analysis for various dimensions of HQDCI in western China in 2019. Note: This map contains only the 12 regions of western China relevant to the research and is not a complete map of China.

Figure 17. The results of LISA cluster analysis for various dimensions of HQDCI in western China in 2019. Note: This map contains only the 12 regions of western China relevant to the research and is not a complete map of China.

Table 1. The evaluation index system for HQDCI.

Overall Goal	Dimension	Secondary Index
High-quality development of the construction industry (A)	Industry scale (B1)	Output value of construction (C1)
		Total value of contracts by construction enterprises (C2)
		Asset-liability ratio (C3)
		Number of construction enterprises (C4)
	Industry structure (B2)	Structure of enterprise qualification (C5)
	Industry structure (B2)	Structure of enterprise type (C6)
	Output efficiency (B3)	Capital profit margin (C7)
		Per capita profit (C8)
		Labor productivity (C9)
	Innovation drive (B4)	Equipment condition (C10)
		Number of researchers (C11)
		R&D investments (C12)
		Scientific payoffs (C13)
	Energy saving and emission reduction (B5)	Energy consumption (C14)
	Energy saving and emission reduction (B5)	Construction material consumption (C15)
	Contribution to society (B6)	Increase in tax revenue (C16)
	Contribution to society (B6)	Output value outside the province (C17)

Table 2. The formulas of the catastrophe model and the state and control variables.

Mutation Model	State Variable	Control Variable	Potential Function	Normalized Formula
cusp catastrophe	1	2	$F (x) = x^{4} + a x^{2} + b x$	$x_{a} = \sqrt{a}, x_{b} = \sqrt[3]{b}$
swallowtail catastrophe	1	3	$F (x) = x^{5} + a x^{3} + b x^{2} + c x$	$x_{a} = \sqrt{a}, x_{b} = \sqrt[3]{b}, x_{c} = \sqrt[4]{c}$
butterfly catastrophe	1	4	$F (x) = x^{6} + a x^{4} + b x^{3} + c x^{2} + d x$	$x_{a} = \sqrt{a}, x_{b} = \sqrt[3]{b}$ , $x_{c} = \sqrt[4]{c}, x_{d} = \sqrt[5]{d}$
The derived catastrophe	1	6	$F (x) = x^{8} + a x^{6} + b x^{5} + c x^{4} + d x^{3} + e x^{2} + f x$	$x_{a} = \sqrt{a}, x_{b} = \sqrt[3]{b}$ , $x_{c} = \sqrt[4]{c}, x_{d} = \sqrt[5]{d}$ , $x_{e} = \sqrt[6]{e}, x_{f} = \sqrt[7]{f}$

Table 3. Relative weight sort.

Corresponding Upper-Layer Indicator	Index	Internal Ordering Under the Same Parent Index
Industry scale (B1)	Output value of construction (C1)	1
	Total value of contracts by construction enterprises (C2)	2
	Asset-liability ratio (C3)	4
	Number of construction enterprises (C4)	3
Industry structure (B2)	Structure of enterprise qualification (C5)	1
Industry structure (B2)	Structure of enterprise type (C6)	2
Output efficiency (B3)	Capital profit margin (C7)	2
	Per capita profit (C8)	3
	Labor productivity (C9)	1
Innovation drive (B4)	Equipment condition (C10)	1
	Number of researchers (C11)	4
	R&D investments (C12)	3
	Scientific payoffs (C13)	2
Energy saving and emission reduction (B5)	Energy consumption (C14)	2
Energy saving and emission reduction (B5)	Construction material consumption (C15)	1
Contribution to society (B6)	Increase in tax revenue (C16)	1
Contribution to society (B6)	Output value outside the province (C17)	2
High-quality development of the construction industry (A)	Industry scale (B1)	2
	Industry structure (B2)	4
	Output efficiency (B3)	3
	Innovation drive (B4)	1
	Energy saving and emission reduction (B5)	5
	Contribution to society (B6)	6

Table 4. The determination of relationships between indicators.

Index Hierarchy	Indicators Within the Same Level	Internal Relationship
Dimension	B1, B2, B3, B4, B5, B6	Complementary
Secondary index	C1, C2, C3, C4	Complementary
	C5, C6	Non-complementary
	C7, C8, C9	Complementary
	C10, C11, C12, C13	Complementary
	C14, C15	Non-complementary
	C16, C17	Non-complementary

Table 5. Iteration situation.

Number of Iterations	Cluster Center
Number of Iterations	Category 1	Category 2	Category 3	Category 4	Category 5
1	0.005	0.013	0.009	0.021	0.000
2	0.000	0.003	0.001	0.002	0.000
3	0.005	0.010	6.448 × 10⁻⁵	0.000	0.000
4	0.003	0.005	5.373 × 10⁻⁶	1.548 × 10⁻⁵	0.000
5	0.000	0.001	4.477 × 10⁻⁷	1.407 × 10⁻⁶	0.000
6	3.526 × 10⁻⁵	0.000	3.731 × 10⁻⁸	1.279 × 10⁻⁷	0.000
7	3.918 × 10⁻⁶	1.578 × 10⁻⁵	3.109 × 10⁻⁹	1.163 × 10⁻⁸	0.000
8	4.354 × 10⁻⁷	2.255 × 10⁻⁶	2.591 × 10⁻¹⁰	1.057 × 10⁻⁹	0.000
9	4.837 × 10⁻⁸	3.221 × 10⁻⁷	2.159 × 10⁻¹¹	9.612 × 10⁻¹¹	0.000
10	5.375 × 10⁻⁹	4.601 × 10⁻⁸	1.799 × 10⁻¹²	8.738 × 10⁻¹²	0.000
11	5.972 × 10⁻¹⁰	6.573 × 10⁻⁹	1.499 × 10⁻¹³	7.946 × 10⁻¹³	0.000
12	6.636 × 10⁻¹¹	9.390 × 10⁻¹⁰	1.266 × 10⁻¹⁴	7.228 × 10⁻¹⁴	0.000
13	7.373 × 10⁻¹²	1.341 × 10⁻¹⁰	9.992 × 10⁻¹⁶	6.550 × 10⁻¹⁵	0.000
14	8.191 × 10⁻¹³	1.916 × 10⁻¹¹	1.110 × 10⁻¹⁶	5.551 × 10⁻¹⁶	0.000
15	9.104 × 10⁻¹⁴	2.738 × 10⁻¹²	0.000	0.000	0.000
16	1.021 × 10⁻¹⁴	3.911 × 10⁻¹³	0.000	0.000	0.000
17	9.992 × 10⁻¹⁶	5.584 × 10⁻¹⁴	0.000	0.000	0.000
18	2.220 × 10⁻¹⁶	7.994 × 10⁻¹⁵	0.000	0.000	0.000
19	0.000	1.110 × 10⁻¹⁵	0.000	0.000	0.000
20	0.000	1.110 × 10⁻¹⁶	0.000	0.000	0.000
21	0.000	0.000	0.000	0.000	0.000

Table 6. Evaluation results of HQDCI in the western region in 2015, 2017, and 2019.

2015			2017			2019
Region	Evaluation Results	Evaluation Level	Region	Evaluation Results	Evaluation Level	Region	Evaluation Results	Evaluation Level
Guizhou	0.814	Medium	Guizhou	0.875	Relatively high	Guizhou	0.884	Relatively high
Yunnan	0.854	Medium	Yunnan	0.871	Relatively high	Yunnan	0.900	Relatively high
Tibet	0.484	Low	Tibet	0.657	Relatively low	Tibet	0.641	Relatively low
Shaanxi	0.896	Relatively high	Shaanxi	0.913	High	Shaanxi	0.938	High
Gansu	0.847	Medium	Gansu	0.850	Medium	Gansu	0.862	Medium
Qinghai	0.651	Relatively low	Qinghai	0.650	Relatively low	Qinghai	0.653	Relatively low
Ningxia	0.655	Relatively low	Ningxia	0.655	Relatively low	Ningxia	0.662	Relatively low
Xinjiang	0.825	Medium	Xinjiang	0.819	Medium	Xinjiang	0.854	Medium
Guangxi	0.836	Medium	Guangxi	0.853	Medium	Guangxi	0.892	Relatively high
Inner Mongolia	0.691	Relatively low	Inner Mongolia	0.701	Relatively low	Inner Mongolia	0.684	Relatively low
Chongqing	0.886	Relatively high	Chongqing	0.908	Relatively high	Chongqing	0.921	High
Sichuan	0.922	High	Sichuan	0.944	High	Sichuan	0.967	High

Table 7. The results of the local spatial correlation analysis.

	Overall Goal	Industry Scale	Industry Structure	Output Efficiency	Innovation Drive	Energy Saving and Emission Reduction	Contribution to Society
Region	Overall Goal	Industry Scale	Industry Structure	Output Efficiency	Innovation Drive	Energy Saving and Emission Reduction	Contribution to Society
Guizhou	H-H	H-H	H-H	H-H	H-H	H-H	H-H
Yunnan	H-H	H-H	H-H	H-H	H-L	H-H	H-H
Tibet	L-H	L-H	L-H	H-L	L-H	H-H	H-H
Shaanxi	H-L	H-H	H-H	H-L	H-H	H-L	H-L
Gansu	H-L	L-L	L-L	H-L	L-H	H-L	H-L
Qinghai	L-H	L-L	L-L	L-H	L-L	H-H	H-H
Ningxia	L-H	L-L	L-L	L-H	L-H	L-L	L-L
Xinjiang	H-L	L-L	L-L	L-H	L-L	H-H	H-H
Guangxi	H-H	H-H	H-H	L-H	L-H	H-H	H-H
Inner Mongolia	L-L	L-L	L-L	L-L	L-H	L-H	L-H
Chongqing	H-H	H-H	H-H	H-H	H-H	H-H	H-H
Sichuan	H-H	H-L	H-L	L-H	H-L	H-H	H-H

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Xiang, Y.; Yin, H.; Wei, Y.; Su, Y. Evaluation and Spatial Evolution Analysis of High-Quality Development in China’s Construction Industry Utilizing Catastrophe Progression Method: A Case Study of Twelve Provinces in the Western Region. Sustainability 2024, 16, 10879. https://doi.org/10.3390/su162410879

AMA Style

Xiang Y, Yin H, Wei Y, Su Y. Evaluation and Spatial Evolution Analysis of High-Quality Development in China’s Construction Industry Utilizing Catastrophe Progression Method: A Case Study of Twelve Provinces in the Western Region. Sustainability. 2024; 16(24):10879. https://doi.org/10.3390/su162410879

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Xiang, Yong, Hao Yin, Yao Wei, and Yangyang Su. 2024. "Evaluation and Spatial Evolution Analysis of High-Quality Development in China’s Construction Industry Utilizing Catastrophe Progression Method: A Case Study of Twelve Provinces in the Western Region" Sustainability 16, no. 24: 10879. https://doi.org/10.3390/su162410879

APA Style

Xiang, Y., Yin, H., Wei, Y., & Su, Y. (2024). Evaluation and Spatial Evolution Analysis of High-Quality Development in China’s Construction Industry Utilizing Catastrophe Progression Method: A Case Study of Twelve Provinces in the Western Region. Sustainability, 16(24), 10879. https://doi.org/10.3390/su162410879

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Evaluation and Spatial Evolution Analysis of High-Quality Development in China’s Construction Industry Utilizing Catastrophe Progression Method: A Case Study of Twelve Provinces in the Western Region

Abstract

1. Introduction

2. Literature Review

2.1. Research on the Influencing Factors of Construction Industry Development

2.2. Research on High-Quality Development of the Construction Industry in China

3. Methodology

3.1. High-Quality Development of the Construction Industry

3.1.1. Concept and Connotation

3.1.2. Index System

3.2. Catastrophe Progression Method

3.2.1. Data Processing

3.2.2. Selection of Corresponding Model

3.2.3. Interval Partition

3.3. Spatial Statistical Theory

3.3.1. Spatial Weight

3.3.2. Moran’s Index

4. Case Studies

4.1. Selection of Area and Time

4.2. Data Source and Preprocessing

4.3. Evaluation Results

4.4. Spatial Evolution Analysis

4.4.1. Spatial Distribution of High-Quality Development Evaluations in Construction

4.4.2. Spatial Correlation Analysis

5. Discussion

6. Conclusions

Author Contributions

Funding

Institutional Review Board Statement

Informed Consent Statement

Data Availability Statement

Conflicts of Interest

Appendix A

Appendix A.1. Initial Indicator Information

Appendix A.2. The Original Data of High-Quality Development of the Construction Industry in Western China in 2015

Appendix A.3. The Original Data of High-Quality Development of the Construction Industry in Western China in 2017

Appendix A.4. The Original Data of High-Quality Development of the Construction Industry in Western China in 2019

Appendix A.5. Data Source

References

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