Evaluating High-Quality Development in the Construction Industry via the Matter–Element Extension Method: A Case Study of 11 Cities in Zhejiang, China

Abstract

China’s construction industry is facing increasing pressure for transformation and upgrading, with high-quality development becoming an essential goal. However, the precise definition and evaluation criteria for high-quality development remain ambiguous. Against this backdrop, this study focuses on 11 cities in Zhejiang province to explore the connotations of high-quality development in the construction industry and establishes an evaluation index system for assessing it. This study employs the entropy weight method and matter–element extension method to evaluate the high-quality development levels of the construction industry across these cities. The empirical results reveal that the development of the construction industry in Zhejiang province is uneven across cities, with significant potential for overall improvement. This study validates the proposed index system and evaluation model while supplementing empirical evidence; it also enriches the literature and provides both theoretical and technical support for advancing the construction industry’s transition toward high-quality development.

1. Introduction

Since the reform and opening-up in 1978, China’s construction industry has experienced average annual growth rates of 12% in total output [1], allowing it to become a key pillar of the country’s economic and social development. The quality of this growth is crucial not only to the wellbeing of its people but also to ensuring the industry’s long-term sustainability [2]. However, over the past decade, the growth rate of China’s construction industry has been declining. In 2017, the Chinese government introduced the concept of “high-quality development”, emphasizing it as the primary task in building a modern socialist country [3]. This pronouncement marked a shift from an era of rapid economic growth to one focused on high-quality development, characterized by innovation, coordination, sustainability, openness, and shared prosperity—an evolution of the sustainable development concept [4,5].

In recent years, constructing a high-quality development evaluation system has become a research hotspot [6]. Evaluating the level of high-quality development in the construction industry is essential for understanding its current status and is a crucial step in implementing a high-quality development strategy. While current policies have outlined general goals for high-quality development in the construction industry [7], clear, detailed guidelines regarding its specific definition and evaluation are still lacking. Moreover, although some scholars have explored the connotations and evaluation of high-quality development in the construction industry [8], most of them have summarized only existing evaluation indicators through literature reviews. These studies are not fully aligned with the concept of high-quality development; they provide only limited practical guidance for the industry. Hence, an evaluation indicator system that can reflect the recent innovative transformations occurring in the construction industry must be developed to improve measurement mechanisms and offer guidance for high-quality development.

Earmarked at CNY 2.386 trillion in 2022, Zhejiang ranked second in China in terms of total construction output value. The value added by the construction industry and its contribution to tax revenue each accounted for over 5.5% of the province’s total. Despite its large scale, the construction sector in Zhejiang has faced significant challenges in recent years, particularly in areas related to industry concentration, profitability, and openness. Similar to other provinces, the construction sector has slowed in growth due to its reliance on rapid expansion. The extensive growth model is no longer sustainable. This gap raises critical questions, such as how to measure the high-quality development of the construction industry, how to evaluate the effectiveness of efforts to promote high-quality development, and how to advance such development in Zhejiang’s construction sector.

This study focuses on the construction industry in Zhejiang. By first defining the connotation of high-quality development in the construction industry, the study constructs an evaluation indicator system to empirically assess the high-quality development levels of the sector across 11 cities in the province. On the basis of these findings, this study proposes countermeasures and recommendations to guide the high-quality development of the industry. In particular, this study focuses on four main objectives: (1) explore the connotations of high-quality development in the construction industry through a literature review; (2) develop evaluation indicators for high-quality development on the basis of surveys, interviews, and expert consultations and determine corresponding weights to construct an evaluation indicator system; (3) assess the high-quality development level of the construction industry in 11 cities across the province; and (4) propose targeted countermeasures and recommendations on the basis of the actual conditions of the construction sector.

The innovations of this study are reflected in three key areas: (1) In the indicator system, 35 measurable indicators across seven dimensions were selected for evaluation on the basis of a review of relevant literature and consultations with scholars, government officials, and practitioners in the construction industry, ensuring high credibility. (2) For the evaluation model, the matter–element extension method was applied for comprehensive evaluation, employing a combination of subjective and objective weighting methods to ensure the reliability of the results. (3) In terms of research scope, this study focuses on Zhejiang, a major construction region in China, and evaluates the development of the construction industry in 11 cities, highlighting regional disparities and providing targeted recommendations to help government departments promote coordinated industry development and reduce regional imbalances.

The research framework of this paper is shown in Figure 1.

2. Literature Review

2.1. Connotation of High-Quality Development in the Construction Industry

High-quality development is a concept unique to China, while research abroad focuses on economic growth [7]. In 2017, the Chinese government introduced the concept of high-quality development, which emphasizes development that effectively meets people’s growing demand for a better life and reflects the principles of the new development paradigm. This form of development is driven primarily by innovation, which is characterized by coordination, guided by green practices, facilitated by openness, and aimed at achieving shared benefits [9]. The transition from rapid growth to high-quality development represents not only a shift in growth patterns and trajectories but also a process of institutional reform and transformation [10].

The concept of high-quality development is inherently multidimensional [11], and scholars have explored various interpretations of these dimensions. The philosophy of “innovation, coordination, green development, openness, and shared prosperity”, introduced in China’s 13th Five-Year Plan, aligns closely with the multidimensional characteristics of high-quality development. These dimensions form the core framework widely adopted by scholars for evaluating high-quality development, serving as a foundation for constructing evaluation systems [10,12,13]. Building on this framework, some scholars have incorporated indicators related to scale growth to create a comprehensive evaluation system for the construction industry [13,14,15]. Accurate measurements of construction product quality [16] and enterprise and product quality are also essential for promoting high-quality development in the construction industry and are frequently included in these evaluation systems.

Several scholars have established frameworks to assess the high-quality development of the construction industry. Wang [17] developed an evaluation system based on seven dimensions—scale growth, growth stability, innovation, green development, coordination, openness, and shared development—to assess the high-quality development of the construction industry across 30 Chinese provinces in 2017. Wang and Li [9] proposed a five-dimensional evaluation framework—profitability, innovation, coordination, green development, and shared prosperity—to measure the high-quality development of the construction industry in Shaanxi province. Deng [16] developed a nine-dimensional framework, including scale, efficiency, stability, product quality, innovation, coordination, green development, and openness, to reveal regional disparities in the high-quality development of China’s construction industry. The literature summarized in

Table 1. Dimensions of high-quality development in the construction industry.

Author(s)	Year	Development Dimensions
Wang & Cheng [13]	2024	Innovation, coordination, green development, openness, and shared development.
Wu & Li [18]	2023	Talent, technology, information, environment, and policy.
Deng et al. [16]	2022	Development scale, development efficiency, development stability, construction product quality, innovation, coordination, green development, and openness.
Sun et al. [19]	2021	Efficiency, quality, safety, and environmental protection.
Li et al. [3]	2020	Quality improvement, efficiency enhancement, innovation driving force, and life cycle sustainability.
Wang, Li & Li [20]	2020	Profitability, innovation, green development, coordination, and shared development.
Wang et al. [17]	2019	Scale growth, growth stability, innovation, coordination, green development, openness, and shared development.

offers important references for evaluating high-quality development in the construction industry.

2.2. Core Indicators of High-Quality Development

The dimensions outlined above serve as key references for understanding and evaluating the high-quality development of the construction industry in this study. Although scholars still have to reach a consensus on the precise connotation of high-quality development in the construction industry, they have generally agreed on several core principles. High-quality development should not focus solely on stable growth in scale but must also embody the implementation of five development concepts: innovation, coordination, green development, openness, and shared benefits. Additionally, high-quality development must ensure quality and safety, continuously enhance development standards, and, ultimately, achieve sustainable growth. This study reviews the literature on industry development levels to deeply identify the evaluation factors and indicators of high-quality development in the construction industry and organizes the indicators into seven primary dimensions: comprehensive benefits, quality and safety, innovative development, green development, coordinated development, open development, and shared development. The specific indicators are shown in

Table 2. Specific indicators for evaluating the development level of the construction industry.

Primary Level	Secondary Level	Third Level	References
Comprehensive benefits	Total scale	Number of employees in the construction industry, number of construction enterprises, contract amount signed by the construction industry, value added by the construction industry, assets of construction enterprises, total profits of construction enterprises, total taxes and profits of construction enterprises, completed floor area, total output of the construction industry/GDP, profit rate of output, per capita taxes and profits, labor productivity calculated by value added, completion rate of buildings, tax-to-output ratio, contribution to related industries, balance of construction loans, current account balance of the construction industry, asset-liability ratio, proportion of loss-making construction enterprises, floor area under construction, return on net assets, and contribution rate of construction industry taxes to total tax revenue.	[9,13,17,19]
	Scale growth	Fixed asset investment growth rate, completed floor area growth rate, completed building floor area growth rate, growth rate of the relative value of employees, growth rate of construction industry profits, urbanization growth rate, value added of the construction industry, growth rate of total output of the construction industry, growth rate of total construction output/fixed asset investment growth rate, construction industry value added/GDP, GDP growth rate, consumption level index, per capita disposable income of urban residents, and industry prosperity.
Quality and safety	Product quality	Number of Luban Awards (National Quality Awards), one-time acceptance pass rate, structural suitability, physical suitability, and excellent product rate of engineering quality.	[8,10]
	Enterprise quality	Adoption of BIM technology, proportion of AAA credit-rated enterprises, number of top 250 contractors globally, and number of top 225 international engineering design companies.
	Production safety	Mortality rate of production safety accidents in housing and municipal projects, relative number of construction market violation cases, and mortality rate per CNY 100 billion of output.
Innovation development	Technological innovation	Technology equipment rate, power installation rate, labor productivity, industrial concentration, technology market transaction value, number of patent applications granted, R&D expenditure/GDP, proportion of R&D expenditure by construction enterprises, full-time equivalent of R&D personnel in the construction industry, scientific and technological achievements of the construction industry, contribution rate of scientific and technological progress, total power of self-owned construction machinery at the end of the year, and net value of self-owned construction equipment.	[9,13,19]
	Talent development	Number of outstanding civil engineering graduates awarded, number of construction science conferences held, number of Zhan Tianyou Awards won, proportion of professional and technical personnel, and number of construction R&D personnel.
Green development	Low-carbon energy savings	Wood consumption per CNY 100 million of output, cement consumption per CNY 100 million of output, glass consumption per CNY 100 million of output, aluminum consumption per CNY 100 million of output, construction solid waste discharge per CNY 100 million of output, total energy consumption per CNY 10,000 of construction output, construction electricity consumption, and steel consumption per CNY 100 million of output.	[9,13,17]
	Green buildings	Average construction noise level, urban sewage treatment capacity per day, green coverage rate in built-up areas, proportion of newly built green buildings, proportion of newly built prefabricated buildings, sewage treatment rate, carbon emission intensity, carbon emissions, construction waste discharge from new building sites, construction waste discharge from prefabricated building sites, air quality rate, PM2.5 concentration, average regional environmental noise level during the day, exhaust gas emissions, industrial wastewater discharge, and industrial solid waste utilization rate.
Coordinated development	Industry balance	Proportion of output from domestic enterprises, proportion of special-grade general contractors, proportion of first-grade professional subcontractors, output ratio of special-grade and first-grade general contractors, output ratio of first-grade professional contractors, proportion of total output from general contractors for housing construction, proportion of total output from specialized construction contractors, proportion of senior professionals in surveying and design institutions, proportion of senior professionals in bidding agencies, proportion of registered professionals at the end of the year in supervision enterprises, revenue of supervision agencies/total output, state-owned enterprise output/total output, construction externalization rate, proportion of subcontracting project output, proportion of technical personnel in bidding agencies, proportion of supervision engineers in supervision enterprises, proportion of first-grade surveying and design units, and proportion of construction consulting enterprises.	[9,13,17,19]
	Industry upgrading	Regional structure deviation coefficient, per capita completed area, per capita output value, per capita construction area, number of surveying and design institutions, number of construction supervision enterprises, industrialization rate, and informatization rate.
Open development	International business	Proportion of foreign-invested enterprises, proportion of output from foreign-invested enterprises, turnover from overseas contracted projects, externalization rate, turnover completed abroad/total turnover of enterprises, proportion of state-owned enterprises in the number of construction enterprises, and proportion of overseas investment output by Chinese construction enterprises.	[9,13,17]
	Domestic business	Proportion of private enterprise output/total output, proportion of output completed in other provinces/total output, and proportion of subcontracting project output/total general contracting output.
Shared development	Welfare distribution	Average annual salary of employees, proportion of taxes from the construction industry, average wage of employees in the construction industry, total output value of the construction industry-value added of the construction industry, average annual income of employees compared with the national average annual income, per capita tax situation, per capita profit, per capita labor compensation, and construction industry main business taxes and surcharges.	[9,13,17,19]
	Shared achievements	Per capita residential area of urban and rural residents, per capita park green space, proportion of completed areas for housing, scientific research, education, medical care, culture, sports, and entertainment, per capita urban road area, number of urban bridges per 10,000 people, green coverage rate in built-up areas, employees at the end of the year, unemployment rate, proportion of construction industry employees/total employment, area of public service buildings as a proportion of construction industry completed area, satisfaction with quality, contribution to social employment, contribution to social finances, and contribution to the social economy.

.

2.3. Methods for Evaluating High-Quality Development

The rational selection and design of comprehensive evaluation methods is crucial for accurately assessing a project’s performance. This study focuses on evaluating the development quality of the construction industry in Zhejiang province. The evaluation framework spans multiple dimensions, incorporating both qualitative and quantitative factors, making the indicator system relatively complex.

Previous studies have employed various methods to assess industrial development quality. For instance, Zhang and Zhang [5] used a composite weighting method to evaluate the development quality of various provinces from 2008 to 2020. Wu and Li [18] applied the entropy weight method and the coupling coordination model to analyze the synergy between smart construction and industrialization in Shenyang. Wang and Wu [2] utilized the SE-SBM (super efficiency slack-based measure) model and gray relational model to explore the spatial and temporal differences, dynamic trends, and driving factors of high-quality development in China’s construction industry from 2006 to 2021. They measured the high-quality development of the construction industry across China’s four major regions from 2009 to 2019, revealing regional disparities.

In previous research on evaluation indicator systems, a common assumption was that all measurable indicators were additive, with final results derived by sequentially aggregating these indicators. However, these studies typically focused on overall evaluation results, often overlooking the assessment of individual dimensions. As a result, identifying specific weaknesses in the development of the construction industry has remained challenging. Traditional evaluation methods, such as fuzzy comprehensive evaluation and gray relational degree evaluation, have been widely used. A summary of the advantages, disadvantages, and application scopes of commonly used comprehensive evaluation methods is presented in

Table 3. Common methods for evaluating the development level of the construction industry.

Method	Description	Advantages and Disadvantages	Application Scope
Fuzzy comprehensive evaluation	This method, which is based on mathematical theory, builds a mathematical model that is based on multiple risk factors for evaluating the research problem.	A single solution is avoided, indicating a good evaluation effect for systems with many complex factors. The impact of objective factors tends to be overlooked, and weighting is highly subjective.	Suitable for indicator systems that are difficult to quantify and have low precision requirements.
Gray relational degree evaluation	This method measures the degree of association between factors based on the similarity or difference in their development trends.	Moderate requirements for sample size and distribution patterns; time-consuming data collection and inability to describe the interrelationship between indicators’ cross-development trends.	Suitable for indicator systems where the source is precise, the relationships are vague, and the sample space is small.
Matter–element extension method	This method uses extension theory and builds models based on matter–elements as the basic units, transforming incompatible contradictions into compatible relationships to achieve optimal goals.	Solves multiobjective conflict problems and builds multiobjective decision-making models, thus reflecting real situations. Moderate difficulty in data processing requirements; high requirements for quantifying both qualitative and quantitative indicators.	Suitable for indicator systems that combine both qualitative and quantitative factors.

.

Cai [21] proposed the matter–element analysis method in the 1980s. Since its development, this method has been widely applied in fields such as safety and quality evaluation [2,22,23,24,25]. The matter–element extension method, which is based on matter–element theory and extension sets [26], reflects the complexity and diversity of practical needs, making it a suitable choice as a quantitative evaluation model for assessing high-quality development in the construction industry. The core of matter–element analysis involves three key components: the object, its characteristics, and corresponding values. By applying matter–element transformations, the method investigates the laws governing changes in objects and addresses contradictions [24].

This study focuses on evaluating the development quality of the construction industry in Zhejiang province, which is a complex and systematic issue. Given that the evaluation indicator system for industry development quality includes both qualitative and quantitative indicators and that many of these indicators involve uncertainty, the matter–element extension method was selected to ensure highly accurate results. Traditional matter–element extension models typically rely on a single subjective weighting method, which has certain limitations. Adopting a combined subjective and objective weighting approach helps mitigate the potential biases stemming from a lack of expert knowledge [27]. In this study, the weights for the first- and second-level dimensions are determined via expert scoring, whereas the weights for measurable indicators are calculated via the entropy weight method. The entropy weight method effectively utilizes information entropy from the data [28], thereby reducing the uncertainty and randomness associated with subjective opinions, and is one of the most robust techniques for determining objective weights.

Although this study focuses on high-quality development in China’s construction industry, its findings are highly relevant on a global scale. The concept of high-quality development is not unique to China; construction industries worldwide face similar challenges, such as accelerated urbanization, the impacts of climate change, and the urgency of resource sustainability. The framework and evaluation system proposed in this study can serve as a reference for assessing construction quality in other regions, adapting to local contexts and needs to promote sustainable development in the global construction sector. Furthermore, the methods employed in this study, such as the matter–element extension method and entropy weight analysis, are broadly applicable. These methodologies provide structured approaches for handling the complexity and uncertainty involved in evaluating construction quality, particularly in areas such as resource efficiency, green building, and technological innovation. As such, the tools and frameworks developed in this research offer practical utility for stakeholders in construction industries worldwide, contributing to the global discourse on sustainable development.

The current research gaps are mainly reflected in the following aspects: (1) In the construction of the index system, current research often ignores the comprehensive opinions of multiple stakeholders, resulting in the constructed index system failing to fully reflect the actual situation and needs of the construction industry. In addition, the existing research lacks specific indicators closely related to the development of the construction industry, which limits the comprehensiveness and practicality of the evaluation. (2) In the application of the evaluation model, especially in the calculation of weights, most studies only use subjective or objective weighting methods. This single weighting method may lead to unreliable indicator weights, thereby affecting the accuracy and credibility of the final evaluation results. Therefore, the introduction of a combination of subjective and objective weighting methods will help improve the effectiveness of the evaluation model. (3) In terms of research scope, there are relatively few in-depth studies on the differences in the development of the construction industry in a specific region and its influencing factors, especially the lack of targeted research to provide specific decision-making support for government departments. Existing research often focuses on the macro level, and the differences between cities in the region and the reasons behind them are insufficiently analyzed, which makes the government lack sufficient basis for promoting the coordinated development of the construction industry and narrowing the regional gap. Therefore, filling these research gaps will not only provide a more scientific and reasonable evaluation framework for the high-quality development of the construction industry, but also provide strong theoretical support and policy recommendations for regional coordinated development and promote the sustainable development of the construction industry.

3. Data and Methods

3.1. Research Object and Data

This study focuses on Zhejiang province as the empirical subject, utilizing construction-related data from 11 cities within the province: Hangzhou, Ningbo, Wenzhou, Jinhua, Shaoxing, Taizhou, Jiaxing, Quzhou, Huzhou, Lishui, and Zhoushan. Zhejiang province was selected as the focus of this study for several key reasons. First, Zhejiang’s economic vitality makes it a valuable subject for research. As one of the most economically developed provinces in China with a rapidly growing economy, its construction industry is among the most advanced in the country, playing a crucial role in local economic growth and attracting investment. Second, the province’s diverse industrial structure allows for a more comprehensive study. Zhejiang has a strong industrial base that includes both traditional and emerging sectors in the construction industry. Modern construction industrialization, new urbanization, and green building are all developing rapidly, enabling us to consider the influence of various factors on high-quality development. Third, regional coordination and development across Zhejiang’s cities, such as Hangzhou, Ningbo, and Wenzhou, exhibit significant differences in economic development, policy implementation, and market demand. Studying these variations helps reveal regional characteristics and challenges in the construction industry, providing empirical evidence for policy making. Additionally, policy support in Zhejiang has been robust, with numerous initiatives aimed at promoting high-quality development in the construction sector, focusing on improving technological standards and environmental sustainability. These policies provide a favorable backdrop for conducting this research. Lastly, Zhejiang’s geographical advantage as a southeastern coastal province with convenient transportation and frequent economic activity places it at the forefront of national construction industry developments, reflecting broader market trends and changes.

The data required to evaluate the high-quality development of the construction industry were sourced primarily from Zhejiang Provincial and municipal statistical yearbooks, construction industry development reports, publicly available statistics from the Housing and Urban–Rural Development Commission, related public listings, and statistical data provided by relevant governmental departments.

The timeframe for the data selection is based on 2021, with two key considerations. First, this study aims to assess the current development quality of the construction industry and provide insights for guiding future stages of development. Second, compared with other sources, the data available for 2021 are more comprehensive and detailed, making them easier to access, thereby ensuring the accuracy of the data and the reliability of the results.

3.2. Research Methods

The matter–element extension model is grounded in matter–element theory and extension mathematics [29]. A matter–element is defined as an ordered triplet consisting of “object, characteristic, and value” [30,31]. The core principle of the matter–element extension evaluation method is to use matter–elements to describe the objects of study. The application process of this method can be summarized in three main steps. First, measurable indicators are divided into quality levels on the basis of industry standards or expert consultation, and criteria are established for each level. Second, the indicator data of each evaluation object are input into the evaluation model. Finally, the correlation degree between each measurable indicator and the criteria for each level is calculated. On the basis of these results, the corresponding level of each measurable indicator can be determined. Then, the weights of the measurable indicators are combined to obtain a comprehensive correlation degree.

(1)

Determining the classical matter–element matrix

The classical matter–element matrix consists of measurable indicators from the evaluation system for high-quality development in the construction industry and their corresponding value ranges. Let the evaluation system for high-quality development in the construction industry consist of n measurable indicators, and the final evaluation results be divided into x levels. The classical matter–element matrix R_xj is established as follows:

$$R_{xj}=\left(Z_{xj},B_i,W_{xji}\right)=\left[\begin{array}{ccc}{Z}_{xj}& B_1& W_{xj1}\\ & B_2& W_{xj2}\\ & \cdots & \cdots \\ & B_n& W_{xjn}\end{array}\right]=\left[\begin{array}{ccc}{Z}_{xj}& B_1& \left(a_{xj1},b_{xj1}\right)\\ & B_2& \left(a_{xj2},b_{xj2}\right)\\ & \cdots & \cdots \\ & B_n& \left(a_{xjn},b_{xjn}\right)\end{array}\right],$$

(1)

where Z_xj represents the j-th grade matrix of construction industry development quality; B_i represents the matrix composed of measurable indicators; W_xji represents the actual value of the measurable indicator, which is the interval formed by the minimum value a_xji and the maximum value b_xji of the i-th measurable indicator of 11 cities in this study, where a_xji and b_xji are the two ends of the interval, respectively; $j=1, 2,\cdots x;\mathrm{i}=1,2,\cdots ,n.$

(2)

Determining the joint domain element matrix

On the basis of the actual values of each measurable indicator in the evaluation system for high-quality development in the construction industry and considering the grade standard ranges determined in the previous step, the corresponding value range of the measurable indicators for each evaluation grade is determined. Since the indicators selected for this topic do not involve technical parameters and there are no relevant standards and specifications for reference, considering the rationality of the evaluation results, the approach here is as follows: overall, the number of places for each measurable indicator in the five levels of I–V is 3, 2, 2, 2, and 2, respectively. That is, for each measurable indicator value, the top three from large to small are level I, the fourth and fifth are level II, and so on. Finally, the node element matrix R₀ of the high-quality development evaluation system of the construction industry is established:

$$R_0=\left(L,B_i,T_i\right)=\left[\begin{array}{ccc}L& B_1& T_1\\ & B_2& T_2\\ & \cdots & \cdots \\ & B_n& T_n\end{array}\right],$$

(2)

where $L$ represents the quality evaluation level of construction industry development (divided into five levels from high to low: I, II, III, IV, and V); T_0i represents the value range of indicator B_i at level $L$; a_0i, b_0i represent the maximum and minimum values of T_0i; i = 1, 2, ⋯, n.

(3)

Determining the matter–element to be evaluated

The matrix composed of the actual values of the measurable indicators of the construction industry in each region is the object–element matrix to be evaluated. According to the actual values of the measurable indicators of the construction industry in each region, the object–element matrix R_q of the evaluation system for high-quality development of the construction industry is established:

$$R_q=\left(Z,B_i,T_i\right)=\left[\begin{array}{ccc}Z& B_1& T_1\\ & B_2& T_2\\ & \cdots & \cdots \\ & B_n& T_n\end{array}\right],$$

(3)

where $Z$ represents the quality of construction industry development; T_i represents the specific data of B_i; i = 1, 2, ⋯, n.

(4)

Determining the weights of indicators

The weights of the indicators are crucial for the scientific and impartial nature of the evaluation system and are an important component of the evaluation framework. These weights represent the significance of each indicator within the overall evaluation system. The rational assignment of weights is essential for the accuracy of the final evaluation results [30].

In the academic field, many methods can be used to assign weights to multiple indicators, which can be broadly categorized into two main types: subjective weighting methods and objective weighting methods. The indicator system constructed in this study consists of three levels, with each level having different characteristics and, thus, requiring different research methods. Therefore, a combination of both subjective and objective methods is used to determine the weights. The primary and secondary dimensions form the overall framework, and their weights are determined through expert scoring. The tertiary indicators are specific measurable indicators, and their weights are determined via the entropy weight method on the basis of the collected statistical data. The final comprehensive weight for each measurable indicator is obtained by multiplying the weights of the tertiary indicators by the weights of their corresponding primary and secondary dimensions.

Here, w_f and w_s represent the weights of the primary and secondary dimensions, respectively. The experts assign scores ranging from 1 to 5 on the basis of importance, and the average score for each dimension is calculated after the individual scores are summed. The entropy weight method is applied to calculate the weights of the measurable indicators as follows:

• Standardize the indicators.

Standardization is performed to eliminate the impact of different dimensions across indicators. All the indicators in this evaluation system are positive indicators.

$$x_{si}={\displaystyle \frac{x_{si}^{\prime }-\min \left(x_{s1}^{\prime },x_{s1}^{\prime }\cdots x_{sn}^{\prime }\right)}{\mathrm{m}\mathrm{a}\mathrm{x}\left(x_{s1}^{\prime },x_{s1}^{\prime }\cdots x_{sn}^{\prime }\right)-\min \left(x_{s1}^{\prime },x_{s1}^{\prime }\cdots x_{sn}^{\prime }\right)}}$$

(4)

where $x_{si}$ is the standardized value; $x_{si}$’ is the original value of the indicator, which is the standardized indicator value; i = 1, 2, ⋯, n.

b.

Calculate the contribution degree of the $i$-th measurable indicator in the $s$-th second-level dimension.

$$P_{si}={\displaystyle \frac{x_{si}}{\sum _i^n{x}_{si}}}$$

(5)

where $P_{si}$ represents the contribution of the i-th measurable indicator value to the s-th secondary dimension; $x_{si}$ is the standardized value; i = 1, 2, ⋯, n.

c.

Calculate the entropy value.

$$e_{si}=-{\displaystyle \frac{1}{\ln n}}\sum _{i=1}^n{P}_{si}\ln \left(P_{si}\right),e_i\ge 0$$

(6)

where $e_{si}$ represents the entropy value of the i-th measurable indicator value in the s-th secondary dimension; $P_{si}$ represents the indicator contribution of the i-th measurable indicator value in the s-th secondary dimension; i = 1, 2, ⋯, n.

d.

Calculate the coefficient of variation.

$$g_{si}=1-e_{si}$$

(7)

where $g_{si}$ is the coefficient of difference of the i-th measurable indicator value in the s-th secondary dimension; $e_{si}$ represents the entropy value of the i-th measurable indicator value in the s-th secondary dimension; i = 1, 2, ⋯, n.

e.

Calculate the weight of the measurable indicator.

$$w_{si}={\displaystyle \frac{g_{si}}{\sum _i^n{g}_{si}}}$$

(8)

where $w_{si}$ is the weight of the i-th measurable indicator value in the s-th secondary dimension; $g_{si}$ is the difference coefficient of the i-th measurable indicator value in the s-th secondary dimension; i = 1, 2, ⋯, n.

The comprehensive weight of the indicators is then determined by multiplying the following weights:

$$w_i=w_f\times w_s\times w_{si}$$

(9)

where $w_f$ and $w_s$ are the weights of the first-level dimension and the second-level dimension, respectively; $w_{si}$ is the weight of the i-th measurable indicator value in the s-th second-level dimension; i = 1, 2, ⋯, n.

(5)

Determining the degree of association between the matter–element to be evaluated and the classic matter–element

The degree of association refers to the extent to which the matter–element to be evaluated conforms to the classic matter–element, specifically the extent to which each indicator in the construction industry high-quality development evaluation system aligns with various grade intervals. The degree of association is calculated as follows:

$$y_i\left(T_i\right)=\left\{\begin{array}{c}-{\displaystyle \frac{s\left(T_i,W_{xji}\right)}{\left|W_{xji}\right|}} T_i\in U_{xji}\\ {\displaystyle \frac{s\left(T_i,W_{xji}\right)}{s\left(T_i,W_{pj}\right)-s\left(T_i,W_{xji}\right)}} T_i\notin U_{xji}\end{array}\right.$$

(10)

$$W_{xji}=\left|b_{ij}-a_{ij}\right|,\left(i=\mathrm{1,2},\cdots ,n;j=\mathrm{1,2},\cdots ,x\right)$$

(11)

$$s\left(T_i,W_{xji}\right)=\left|T_i-{\displaystyle \frac{1}{2}}\left(a_{xji}+b_{xji}\right)\right|-{\displaystyle \frac{1}{2}}\left(b_{xji}-a_{xji}\right)$$

(12)

$$s\left(T_i,W_{pj}\right)=\left|T_i-{\displaystyle \frac{1}{2}}\left(a_{0i}+b_{0i}\right)\right|-{\displaystyle \frac{1}{2}}\left(b_{0i}-a_{0i}\right)$$

(13)

(6)

Calculating the comprehensive relevance degree

The comprehensive degree of relevance represents the degree of alignment between the overall evaluation goal of construction industry development quality and each evaluation grade. This degree can be obtained by combining the weight values of each measurable indicator with the degree of relevance for each grade, as shown in the following formula:

$$Y_j\left(R_q\right)={\sum }_{i=1}^n{w}_i{y}_i\left(T_i\right).$$

(14)

The calculated results are further standardized, and the values of the comprehensive correlation are observed. The level corresponding to a value of 1 represents the quality level of the development of the construction industry.

$$\overline{Y_j\left(R_q\right)}={\displaystyle \frac{y_j\left(R_q\right)-\min \left(y_j\left(R_q\right)\right)}{\mathrm{m}\mathrm{a}\mathrm{x}\left(y_j\left(R_q\right)\right)-\mathrm{m}\mathrm{i}\mathrm{n}(y_j\left(R_q\right)}}$$

(15)

4. Results and Discussion

4.1. Defining the Connotations of High-Quality Development in the Construction Industry

The high-quality development of the construction industry in Zhejiang province should align with the new development ideas of “innovation, coordination, green development, openness, and shared development”. This philosophy emphasizes the optimal balance between scale and efficiency to enhance the core competitiveness of enterprises, ensure product quality, improve the safety and efficiency of construction processes, and promote green and sustainable building practices. Technological and managerial innovations should serve as the key drivers, fostering coordination and integration both within the industry and across related sectors. Expanding the construction market and ensuring the equitable distribution of development benefits among stakeholders are also crucial. In this manner, the construction industry can achieve comprehensive improvements in economic, social, and environmental outcomes, ultimately supporting long-term sustainable development.

On the basis of these principles and drawing from existing theories on high-quality economic development, this study proposes a conceptual model for high-quality development in the construction industry (Figure 2).

The high-quality development of the construction industry is a multidimensional project encompassing seven key dimensions: comprehensive benefits, quality and safety, innovation-driven development, green development, coordinated development, open development, and shared development. These dimensions collectively provide the foundation for the industry to achieve sustainable, efficient, and high-quality growth.

First, comprehensive benefits focus on optimizing resource allocation and improving production efficiency to maximize economic returns. Quality and safety serve as the core foundations of industry development, ensuring reliable construction outcomes and user satisfaction through rigorous quality management and safety protocols. Innovation-driven development promotes technological advancement and management innovation, driving the industry’s overall competitiveness. Green development emphasizes environmental protection and resource conservation, advancing sustainability within the construction sector. Coordinated development aims to optimize industry structure and foster multilevel coordination, enhancing overall industry competitiveness. Open development encourages regional and international cooperation, fostering the global integration of the construction industry. Finally, shared development prioritizes social equity and the fair distribution of benefits, ensuring that the gains from industry growth are widely shared across society.

The integration of these seven dimensions forms a holistic framework for high-quality development, guiding the industry toward a sustainable and comprehensive future. These seven dimensions reflect the essential requirements for development goals, processes, and methods within the construction industry. These elements are interrelated and progressively connected, collectively determining the path toward high-quality development. Development goals focus on optimizing overall quality to enhance competitiveness. Development processes emphasize improving efficiency and fostering coordination to ensure optimal resource utilization. Development methods call for deep industry transformation, driving innovations in both technology and management. These three aspects complement and reinforce each other, forming a cohesive roadmap for the construction industry’s pursuit of high-quality development.

4.2. Constructing the Evaluation Index System

Through a literature review and summary, this study preliminarily constructed a framework of the index system for high-quality development of the construction industry in Zhejiang province, which includes seven dimensions: comprehensive scale, quality and safety, innovative development, green development, coordinated development, open development, and shared development. On this basis, multiple rounds of expert interviews were conducted to optimize the index system. Finally, after synthesizing the opinions of all parties, an evaluation index system for high-quality development of the construction industry in Zhejiang province was formed, covering seven categories and a total of 35 indicators (

Table 4. Evaluation index system for high-quality development of the construction industry in Zhejiang province.

Primary Dimension	Secondary Dimension	Indicators
A. Comprehensive benefits	A1. Total scale	A11. Total output value of the construction industry (billion RMB).
		A12. Added value of the construction industry (million RMB).
		A13. Net assets of construction enterprises (billion RMB).
		A14. New contract amount (billion RMB).
	A2. Structural benefits	A21. Proportion of civil engineering output value (%).
		A22. Proportion of engineering general contracting project output (%).
	A3. Efficiency level	A31. Per capita construction area (m2).
		A32. Output value profit margin (%).
		A33. Labor productivity (million RMB/person).
B. Quality and safety	B1. Product quality	B11. Quantity of national awards (items).
		B12. Quantity of provincial awards (items).
	B2. Enterprise quality	B21. Number of demonstration enterprises for modernization of the construction industry in Zhejiang province (items).
		B22. Number of special-grade qualification enterprises (items).
		B23. Number of first-class qualification enterprises (items).
		B24. Number of specialized, sophisticated, unique, and innovative enterprises (items).
	B3. Production safety	B31. Number of provincial standardized construction sites (items).
C. Innovation development	C1. Technological innovation	C11. Number of provincial enterprise technology centers (items).
		C12. Number of municipal enterprise technology centers (items).
		C13. Provincial engineering methods (items).
		C14. Number of provincial new technology demonstration projects (items).
	C2. Talent cultivation	C21. Outstanding builders and project managers (persons).
		C22. Outstanding entrepreneurs in the construction industry (persons).
D. Green development	D1. Green buildings	D11. Proportion of prefabricated building area to new building area (%).
		D12. Number of green demonstration projects (items).
E. Coordinated development	E1. Industry balance	E11. Construction industry concentration (%).
		E12. Proportion of specialized contracting enterprise output (%).
	E2. Personnel structure	E21. Ratio of technical personnel to total personnel (%).
	E3. Industry modernization	E31. Number of new-type construction industrialization industry bases (units).
		E32. Equipment rate (RMB/person).
		E33. Number of smart construction site demonstration projects (items).
F. Open development	F1. External business	F11. Degree of external output value (%).
	F2. International business	F21. Overseas contracting project revenue (billion RMB).
G. Shared development	G1. Welfare distribution	G11. Ratio of the annual income of workers to the social average annual income (%).
	G2. Results sharing	G21. Contribution rate of the construction industry to social employment (%).
		G22. Contribution rate of construction industry tax revenue to total tax revenue (%).

).

Compared with existing indicators, the indicators used in this study are more novel and sophisticated, and can more accurately measure the development level of the construction industry. For example, in the dimension of quality innovation, on the basis of the commonly used indicators such as labor productivity [17], we innovatively selected indicators such as the number of modern demonstration enterprises in Zhejiang province’s construction industry and the number of specialized, refined, and innovative enterprises, aiming to fully reflect the trend of promoting the modernization of construction enterprises and encouraging the development of specialized, refined, and innovative enterprises in recent years. These indicators not only reflect the innovation ability of enterprises, they also reflect the government’s efforts to promote industrial upgrading. In terms of coordinated development, in addition to indicators such as the proportion of special-grade general contracting construction enterprises [20], the number of new building industrialization industrial bases and the number of smart construction site demonstration projects are added as two important evaluation indicators to actively respond to the call for industrial modernization. These indicators not only focus on technological progress in the construction industry, they also emphasize the coordinated integration between industries, which is conducive to the optimal allocation of resources and the improvement of benefits. The indicators in the indicator system constructed in this study are closely centered on the Chinese government’s concept of promoting high-quality development of the construction industry, aiming to provide a scientific basis for relevant decision making, promote the sustainable development of the construction industry, and provide the industry with a more practical and forward-looking evaluation system.

4.3. Calculating the Indicator Weights

(1)

Primary and secondary dimension weights

Expert scoring was employed to determine the weights of the primary and secondary dimensions. We ensured a balanced representation by including experts from academia, industry, and government sectors, all of whom have extensive experience and knowledge in green development and the construction industry. This diverse expertise helps ensure a comprehensive and well-rounded evaluation. A total of 15 questionnaires were distributed, with 14 valid responses received. After a preliminary rationality check on the questionnaires, the reliability of the expert scores was tested, resulting in a Cronbach’s α coefficient of 0.859, indicating high reliability (a Cronbach’s α greater than 0.7 is considered reliable).

(2)

Tertiary indicator weights

The weights of the tertiary indicators, which are measurable indicators, were calculated via the entropy weight method. This method assesses the importance of each indicator on the basis of its variability. A judgment matrix was constructed to determine the objective weights, effectively addressing the issue of inconsistent units across different indicators. Combining the entropy weight method with the matter–element extension method allows for a comprehensive consideration of both qualitative and quantitative indicators, reducing subjective bias in weight determination and resulting in a highly scientific and reasonable evaluation model.

(3)

Comprehensive weights

The comprehensive weights were calculated by multiplying the weights of each measurable indicator by the weights of the corresponding primary and secondary dimensions. The final comprehensive weights are presented in

Table 5. Summary of indicator weights.

Primary Weight	Secondary Weight	Tertiary Weight	Overall Weight
A: 0.1667	A1: 0.3315	A11: 0.2469	0.0136
		A12: 0.2364	0.0131
		A13: 0.2896	0.0160
		A14: 0.2271	0.0125
	A2: 0.3315	A21: 0.6434	0.0356
		A22: 0.3566	0.0197
	A3: 0.3370	A31: 0.3901	0.0219
		A32: 0.2601	0.0146
		A33: 0.3498	0.0197
B: 0.1667	B1: 0.3385	B11: 0.3357	0.0189
		B12: 0.6643	0.0375
	B2: 0.3281	B21: 0.1630	0.0089
		B22: 0.2210	0.0121
		B23: 0.1780	0.0097
		B24: 0.4380	0.0240
	B3: 0.3333	B31: 1.0000	0.0556
C: 0.1538	C1: 0.5039	C11: 0.4231	0.0328
		C12: 0.1628	0.0126
		C13: 0.1436	0.0111
		C14: 0.2706	0.0210
	C2: 0.4961	C21: 0.4440	0.0339
		C22: 0.556	0.0424
D: 0.1462	D1: 1.0000	D11: 0.3496	0.0511
		D12: 0.6504	0.0951
E: 0.1359	E1: 0.3374	E11: 0.4659	0.0214
		E12: 0.5341	0.0245
	E2: 0.3067	E21: 1.0000	0.0417
	E3: 0.3558	E31: 0.2553	0.0123
		E32: 0.2166	0.0123
F: 0.1128	F1: 0.5745	F11: 1.0000	0.0648
	F2: 0.4255	F21: 1.0000	0.0480
G: 0.1179	G1: 0.5096	G11: 1.0000	0.0601
	G2: 0.4904	G21: 0.3775	0.0218
		G22: 0.6225	0.0360

.

4.4. Analyzing the Comprehensive Evaluation Results

The construction industry data and the development goals of Zhejiang province are analyzed by classifying the quality of construction industry development into five levels: I (high quality), II (relatively high quality), III (moderate quality), IV (relatively low quality), and V (low quality).

4.4.1. Overall Evaluation Results and Analysis

The overall evaluation and final ratings of the construction industry across various cities in Zhejiang province are presented in

Table 6. Rating of construction industry development quality in cities in Zhejiang.

City	I	II	III	IV	V	Rating
Hangzhou	1.0000	0.1238	0.0855	0.0000	0.1220	I
Ningbo	1.0000	0.6052	0.3789	0.1073	0.0000	I
Wenzhou	0.0000	0.4314	0.5506	0.6484	1.0000	V
Jinhua	1.0000	0.3646	0.1613	0.0000	0.0257	I
Lishui	0.0000	0.3585	0.5363	0.7331	1.0000	V
Huzhou	0.0000	0.3015	0.6427	1.0000	0.5033	IV
Quzhou	0.0000	0.0940	0.3271	0.5777	1.0000	V
Shaoxing	1.0000	0.4140	0.2169	0.0000	0.3004	I
Zhoushan	0.0000	0.1588	0.3494	0.6726	1.0000	V
Taizhou	0.0000	0.4640	1.0000	0.7203	0.2539	III
Jiaxing	0.0000	0.4787	1.0000	0.8578	0.5598	III

and Figure 3. The evaluation results of construction industry development quality in Zhejiang province reveal significant disparities among its cities. A strong correlation exists between the quality of construction industry development and local economic conditions, with differences in construction quality largely reflecting the economic status of each region. Hangzhou, Ningbo, Jinhua, and Shaoxing have notably higher levels of construction industry development quality compared with the other provinces.

Hangzhou and Ningbo consistently lead other cities in Zhejiang province in terms of economic development, characterized by more advanced and diversified industrial structures. These cities are highly equipped to incorporate modern economic development concepts and technologies, facilitating the promotion of high-quality development in the construction industry. Both Hangzhou and Ningbo have pioneered organizational and technological transformations within the industry, which has helped maintain their high levels of construction industry quality.

In Shaoxing, the construction industry plays a pivotal role in the local economy, serving as a major pillar and competitive advantage. The city’s construction sector is distinguished by its large scale, high output value, leading enterprises, and diverse development pathways. Shaoxing has placed strong emphasis on construction technology, enterprise innovation, and talent recruitment, thereby strengthening its technical and talent foundations for modern construction development. Furthermore, the city has fostered a supportive environment and implemented policy incentives to encourage the high-quality growth of its construction enterprises.

Jinhua, often referred to as the “Home of Craftsmen” and a key player in the construction industry, also regards construction as a pillar of its economy, a source of competitive advantage, and a driver of wealth. In recent years, Jinhua has boldly explored and pioneered initiatives in construction industrialization and modernization, providing valuable insights not only for Zhejiang province but also for the entire country.

4.4.2. Evaluation and Analysis of Primary Indicators

The quality of construction industry development is determined by the integration of various measurable indicators. This study further analyzes the differences across the 11 cities by using seven primary dimensions (comprehensive benefits, quality and safety, innovative development, green development, coordinated development, open development, and shared development) to gain an in-depth understanding of the development quality of the construction industry across cities in Zhejiang province. The ratings of these primary dimensions for high-quality construction industry development in each city are shown in Figure 2.

(1)

Comprehensive benefits

In terms of comprehensive benefits, the ratings of cities in Zhejiang province generally align with the overall development levels of their construction industries. Although Wenzhou received a relatively low overall evaluation, it performed well in terms of comprehensive benefits. Taking into account local economic conditions, Hangzhou and Ningbo, with their relatively large economies and extensive construction activities, clearly stand out. Advanced technologies and effective market governance modes have enabled these cities to excel in terms of scale, structural benefits, and efficiency. Shaoxing, where construction is a key industry, also ranks highly in this dimension. Cities such as Lishui, Quzhou, and Zhoushan could focus on optimizing industrial structures, innovating service models, and enhancing technological innovation to drive higher-quality construction industry development.

(2)

Quality and safety

The quality and safety ratings for Jinhua and Wenzhou differ slightly from their overall development ratings, whereas the ratings for other cities are largely consistent with their overall evaluations. This discrepancy arises because Jinhua has fewer demonstration enterprises for construction industry modernization, with only eight such enterprises—far fewer than cities with the highest overall ratings. By contrast, Wenzhou performs well in terms of provincial awards, the number of provincial demonstration enterprises for construction modernization, and the number of first-class qualified enterprises, earning a third-tier rating in the quality and safety dimension.

(3)

Innovative development

Hangzhou and Ningbo lead in innovation, particularly in technological innovation and talent development, where they hold significant advantages. Shaoxing and Jinhua have also made notable achievements in talent development, boasting high numbers of outstanding construction managers, project leaders, and prominent entrepreneurs. Zhoushan, however, lags behind in technological innovation, with no provincial or municipal technology centers. Across the province, the number of provincial enterprise technology centers remains low, and most cities lack such centers.

(4)

Green development

In the green development dimension, Jinhua and Shaoxing show significant disparities compared with their overall evaluation levels. In 2021, only 23.91% of newly built prefabricated buildings in Jinhua accounted for new building areas, placing them at the bottom of the province. Shaoxing, with only one green demonstration project, received a level V rating for green development. By contrast, Hangzhou, rated level I, led the province in 2021 with 25 recognized green demonstration projects.

(5)

Coordinated development

Coordinated development primarily reflects the balance of industry, personnel structure, and industry modernization. Zhoushan, with its high industrial concentration, high proportion of technical staff, and high equipment rate, achieved a second-tier rating in this dimension. Hangzhou continues to excel, with 25 smart construction site demonstration projects—far surpassing other cities. Jinhua also performs well in coordinated development, having the highest proportion of technical staff (21.61%) in the province, which gives it a notable advantage in personnel structure.

(6)

Open development

The performance of cities in open development is largely consistent with their overall construction industry development levels. Some cities have significantly exceeded the average in terms of external business operations, both domestically and internationally. For example, Jinhua’s external business output ratio reached 58.48%, which means that more than half of its construction industry output comes from contracts outside the province. Hangzhou generated RMB 4.632 billion from overseas contracted projects, surpassing the combined overseas contract revenue of other cities.

(7)

Shared development

In the shared development dimension, some cities show notable differences from their overall construction industry evaluation levels because of the focus on welfare distribution and social outcome sharing within the city. Although Hangzhou scored high in its overall evaluation, its shared development rating is only level V. This is because the average annual income of construction workers in Hangzhou is not particularly competitive relative to the average social income, and the contribution of the construction industry to total tax revenue is relatively low. Similarly, Ningbo’s rating is level V, which is due mainly to the relatively low proportion of construction industry employees and the industry’s tax contribution rate. By contrast, Jinhua, Shaoxing, and Taizhou perform well in this dimension. Zhoushan achieved a second-tier rating because of its higher-than-average salaries for construction workers.

4.4.3. Secondary Dimension Evaluation and Results Analysis

The evaluation results for the secondary dimensions of high-quality construction industry development in the 11 cities of Zhejiang province based on the calculations are presented in

Table 7. Evaluation results of secondary dimensions for high-quality development of the construction industry in Zhejiang.

Secondary Dimension	Hangzhou	Ningbo	Wenzhou	Jinhua	Lishui	Huzhou	Quzhou	Shaoxing	Zhoushan	Taizhou
Total scale	I	I	II	II	V	IV	V	I	V	III
Structural benefits	I	II	II	II	V	IV	V	I	V	III
Efficiency level	III	V	I	III	II	IV	V	V	V	I
Product quality	I	I	II	I	IV	II	V	IV	IV	IV
Enterprise quality	I	I	III	III	V	IV	V	I	V	III
Production safety	I	I	III	I	IV	III	V	IV	V	II
Technological innovation	I	I	III	III	V	IV	V	II	V	II
Talent development	I	II	IV	I	V	III	V	I	IV	IV
Green buildings	I	I	V	V	IV	IV	III	I	III	III
Industry balance	I	I	IV	V	V	I	V	III	II	IV
Personnel structure	V	III	II	I	II	III	I	V	I	IV
Industry modernization	I	I	V	I	V	IV	II	I	V	II
Out-of-province business	II	II	I	I	V	IV	IV	I	V	III
International business	I	I	V	I	III	IV	II	II	IV	III
Welfare distribution	V	IV	V	I	II	IV	V	III	I	I
Outcome sharing	IV	V	II	I	II	IV	II	I	V	III

.

These results reveal which cities have strengths and weaknesses in their construction industries. For instance, the previously identified shortcomings in coordinated development and shared development in Hangzhou can be attributed to issues related to workforce structure and welfare distribution. Similarly, while Ningbo faces some of these challenges, cities such as Jinhua and Shaoxing excel in these areas. This phenomenon highlights the need for Hangzhou and Ningbo to prioritize improvements in workforce structure and ensure that the benefits of high-quality development are equitably distributed.

Despite the overall low quality of construction development in Quzhou and Zhoushan, both cities perform well in terms of workforce structure and welfare distribution. As they continue to develop, maintaining these strengths while addressing other areas in need of improvement is crucial. Ningbo and Shaoxing should focus on enhancing efficiency metrics, such as labor productivity and profit margins, by drawing on the successful experiences of cities such as Wenzhou, Taizhou, and Jiaxing. Conversely, Jinhua should prioritize the implementation of green building practices to ensure higher-quality development in the future.

5. Conclusions

In recent years, the construction industry, a traditional strength of Zhejiang province, has embraced a new development philosophy by adopting modern construction methods and driving high-quality development through innovative forces. This shift has significantly improved industry quality and efficiency overall. On the basis of the characteristics of Zhejiang’s construction sector, this study developed an evaluation system for assessing high-quality development and conducted an empirical analysis at the city level, validating the effectiveness of the evaluation system. The conclusions drawn from this study offer valuable insights for government agencies and construction enterprises.

First, this study explored the connotations of high-quality development in the construction industry and built a corresponding evaluation model. This research established clear requirements from the perspectives of development goals, processes, and methods, aiming to enhance the industry’s economic, social, and environmental benefits while contributing to sustainable societal and economic growth. The connotation of high-quality development was analyzed through seven key dimensions: comprehensive benefits, quality and safety, innovation, green development, coordinated development, openness, and shared development. These dimensions provide a roadmap for achieving high-quality development.

Building on this conceptual model, this study further refined the seven dimensions into an evaluation system comprising 16 secondary indicators and 35 tertiary indicators. The weights of these indicators were determined through expert scoring and the entropy weight method, whereas the matter–element extension method was employed to develop a comprehensive evaluation model. By combining qualitative and quantitative indicators, subjective bias in weight determination could be minimized, allowing this study to enhance the scientific rigor and reliability of the evaluation model.

Additionally, this study conducted an empirical evaluation of the construction industries in 11 cities across Zhejiang province, offering suggestions on the basis of a comparative analysis. The results indicate that the quality of construction industry development in Zhejiang was uneven in 2021. Cities such as Hangzhou, Ningbo, Jinhua, and Shaoxing demonstrated high-quality development; Taizhou and Jiaxing were rated as medium quality; and Huzhou, Wenzhou, Lishui, and Quzhou were classified as low quality. Overall, there remains considerable potential for improvement across the province.

The evaluation of primary and secondary indicators revealed specific areas for improvement. For instance, despite their high overall ratings, Hangzhou and Ningbo need to address issues in welfare distribution and shared development. Hangzhou, Ningbo, and Shaoxing also exhibit weaknesses in workforce structure, whereas Jinhua needs to prioritize green building practices and balanced industry development. In the less-developed regions, Wenzhou excels in efficiency and out-of-province operations, Huzhou in industry balance, Quzhou in workforce structure, and Zhoushan in both workforce structure and welfare distribution. Additionally, Taizhou and Jiaxing stand out in terms of efficiency and welfare distribution. The successes of these cities offer valuable lessons for others.

This study proposes several strategies to address the regional disparities in high-quality development across Zhejiang’s construction industry. First, fostering regional cooperation and coordination is essential. Encouraging collaboration between regions with different levels of development can help integrate resources and connect projects across the province. Highly developed areas can work with less-developed regions to share technologies, such as smart buildings and green construction, increasing the technical standards of the entire industry.

Second, providing technological and innovation support is critical. Offering subsidies and establishing innovation funds in less developed areas can stimulate technological advancements. Government incentives, such as tax breaks and grants, can encourage the adoption of new technologies, helping to bridge the innovation gap between regions.

Third, talent development must be prioritized. Strengthening the cultivation and introduction of skilled professionals in underdeveloped areas can significantly improve the quality of construction. A province-wide mechanism for sharing technical expertise can ensure that experts from developed regions provide regular guidance and collaboration in less developed areas, whereas targeted training programs can help strengthen the talent pool.

Finally, financial support and investment guidance are crucial. Differentiated regional development policies that offer preferential fiscal, tax, and land policies can attract investment to less developed regions. Enhancing financial support, particularly in the form of loans and financing channels, can help construction enterprises in these areas grow and thrive. By strategically directing financial resources, the government can ensure that development is balanced across the province.

This study not only provides theoretical and empirical support for high-quality development in China’s construction industry, it also makes significant contributions to the existing scientific literature. While many previous studies have focused primarily on economic growth, cost control, or productivity in the construction sector, this research offers a more comprehensive perspective on high-quality development, incorporating dimensions such as economic performance, environmental sustainability, technological innovation, and regional coordination. This multidimensional framework addresses a critical gap in the literature by offering a holistic evaluation system that reflects the increasing global demand for sustainable development in the construction industry. Additionally, the use of methods such as the matter–element extension method and entropy weight analysis introduces new methodological tools for evaluating high-quality development in construction. These approaches offer unique advantages in managing complex, multicriteria decision-making processes, and are applicable not only in China but also in global contexts. Finally, this study provides actionable policy recommendations, bridging the gap between theory and practice, and offering concrete guidance for policymakers and industry practitioners aiming to promote high-quality development in construction.

Despite the strengths of this study, there are certain limitations that should be acknowledged. First, the matter–element extension method, while effective in structuring complex evaluations, may not be suitable for scenarios involving highly nonlinear relationships or where subjective judgments significantly influence the results. The method also relies on the availability of well-defined criteria, which may limit its applicability in more uncertain or dynamic environments. Future research could consider employing alternative methods, such as fuzzy comprehensive evaluation or analytic hierarchy process (AHP), to address these challenges and improve the robustness of the evaluation process. Additionally, the scope of this study is geographically limited to Zhejiang province, and further studies could explore broader regional or national contexts to enhance the generalizability of the findings.