Green Development Level Evaluation of Urban Engineering Construction in the Mid-Low Reaches of Yangtze River, China

Abstract

Urban engineering construction represents the physical construction aspects of urban areas and is recognized as an important carrier for green city. With the rapid pace of urbanization, the conventional construction mode is no longer sufficient to meet the requirements of achieving a beautiful China. As a result, promoting the green development for urban engineering construction (GDUC) has become an vital initiative to facilitate the green transformation and sustainable development of cities. This paper adopts the comprehensive evaluation model and super-efficiency slacks-based measure model to evaluate and demonstrate the status and efficiency of GDUC in the Mid-Low reaches of Yangtze River (MLRYR) from 2011 to 2020. The results show a consistent increase in both the status and efficiency of GDUC in the MLRYR during the study period, with a more noticeable changes observed in status than efficiency. In addition, the development of status exhibits distinct phases on the time scale, while the development of efficiency shows prominent differences on the spatial scale. The level stages and significant factors of GDUC are analyzed through a comprehensive evaluation considering two dimensions: status and efficiency. Given these results, in order to further promote the level of regional GDUC in China, several countermeasures and suggestions are put forward from the following aspects: perfecting the status of urban physical construction, enhancing the efficiency of engineering economic production, and strengthening communication and collaboration within urban regions.

1. Introduction

Since the 20th century, the process of global industrialization and urbanization has entered a stage of rapid development, which inevitably caused environmental pollution and ecological destruction alongside economic growth and urban expansion [1,2]. After the 1970s, China has completed the world’s largest and fastest process of urbanization in history, with the level of urbanization rising from 19.39% in 1980 to 63.89% in 2020. However, due to the extensive and chaotic construction model, all kinds of urban diseases were significantly prominent, such as waste of resources, garbage accumulation, and environmental pollution [3]. In 2022, the energy consumption in the field of building accounted for 45.5% of the total social consumption in China, carbon emissions accounted for 50.9%, and the annual output of construction waste exceeded 3 billion tons, highlighting the urgent need for the transformation of urban construction [4]. In October 2021, Chinese official institution issued the Opinions on Promoting the Green Development of Urban and Rural Construction [5], which formally put the green development practice and systemic construction in the field of urban and rural construction into progress. Promoting the green development realization of urban engineering construction has become an important strategic approach to cracking the problem of urban diseases, improving the efficiency of engineering construction, and speeding up the establishment of green cities [6]. Therefore, it holds significant reference value to objectively and scientifically evaluate the green development level of urban engineering construction and comprehensively grasp the green development situation of urban engineering construction in different regions for relevant policy formulation and plan implementation.

The Mid-Low reaches of Yangtze River (MLRYR) is located in the Yangtze River Economic Belt, covering seven provinces of Shanghai, Jiangsu, Zhejiang, Anhui, Jiangxi, Hubei, and Hunan, which is one of the regions with high-density economy and population. With a large scale of urban construction and a deep foundation of engineering industry, the MLRYR serves as an important demonstration area for the construction of green and low-carbon cities [7]. The study area is shown as Figure 1.

In view of the development of urban construction, the current research results primarily focused on two aspects: the concept of green city construction and the measurement of urban green development.

Over the past century of urbanization, scholars and urban planners have been thinking about what kind of urban construction ideas to adopt to solve the endless urbanization problems and have put forward the concepts of garden city [8], circular city [9], ecological [10] city, low-carbon city [11], and green city [12]. Li X et al. [13] believed that the garden city and ecological city represented the goal-oriented city concepts, while the circular city and low-carbon city represented the path-oriented city concepts, through the way cities were designed and developed. The concept of green city integrated these two forms, seeking to achieve harmony and unity between nature and economy, while pursuing the realization of low consumption, low carbon, and high efficiency. Matthew E. K [14] firstly studied the dynamic relationship between urban development and urban environment, identifying these two aspects as the core components of the green city concept. OECD [15] suggested that reducing environmental load and energy consumption can contribute to the development of green economy and create employment opportunities in urban areas, leading to overall prosperity within the green city. Shi F et al. [16] analyzed the elements of the green city and pointed out that the green city included a renewable and efficient energy system, an energy-saving and emission-reduction building system, and a convenient and systematic transportation network that together constituted a healthy and beautiful living system. Ji Q F et al. [17] identified three concepts of urban green development: eco-city, low-carbon city, and low-carbon eco-city. They argued that eco-city focused on nature and living environment, low-carbon city emphasized on carbon emissions reduction, and low-carbon was a combination of both concepts. Zhang M [18] traced the development of green city theory, believed there were mutually influential and beneficial between the green economy and green living environment, and proposed that the green city was a construction model of a new era city, with the prosperous development of the green city and perfection of a green living environment.

In terms of measuring urban green development, Finkelstein L [19] pointed out that the quantification of properties of phenomena or things in the real world can all be called measurement. Liu Y et al. [20] argued that there were five dimensions of green development: status, speed, efficiency, elasticity, and coverage. These dimensions represented green development from five aspects of performance, evolutionary process, idea output, factor influence, and center displacement, with different dimensions requiring specific analytical models. Shi M J et al. [21] constructed the green city index including environment, resources, low carbon economy, and livability and compared it among 25 international cities. In comparison to European regions, they found that the indexes of urban environment and low carbon economy were significantly lower in China, while the CO₂ emissions of urban production and life were generally higher. Liang Z et al. [22] used the input factors of capital, labor, technology, and resources, along with the output factors of regional economy, social welfare, waste treatment, and environment pollutions, to analyze the green development efficiency of cities in China. The research revealed an overall fluctuating upward tendency from 2005 to 2015, and the increase of environmental pollution posed a main obstacle to improving green development efficiency. Qu Y [23] used Data Development Analysis (DEA) model to measure the sustainability efficiency of urban engineering construction, and the results showed that the overall comprehensive efficiency of urban engineering construction sustainability fluctuated initially before increasing from 2008 to 2017 in China. The efficiency in the MLRYR region was expected to get growth in the future. Zhang J et al. [24] studied the effect of technological innovation on urban eco-development efficiency by considering GDP and four pollutant gases as output indicators. The results indicated that technological innovation had a positive effect on urban eco-development efficiency, with a higher promoting effect in the eastern regions of China than the central and western regions.

The existing studies on the development of urban construction, in terms of conceptual content, mainly focused on the analysis of relationships between urban green economy and urban environment, hardly ever considering and discussing the characteristics of material production during urban construction activities. From the perspective of measurement methods, these studies mainly relied on the indicator system to measure the green development status or efficiency unilaterally. This approach resulted in single-dimensional evaluation that was limited by the completeness of indicators.

To fill the above gap, this paper introduces green development theory into urban engineering construction activities, selects the MLRYR as the observation object, and analyzes the concept definition and multidimensional measurement of green development of urban engineering construction (GDUC). The measurement content contains two aspects: green development status and green development efficiency. The measurement object involves the urban areas and urban infrastructure extension areas of seven provinces in the MLRYR from 2011 to 2020. Based on the measurement results of status and efficiency, the level of GDUC in MLRYR is evaluated and displayed at the end. Furthermore, several suggestions for promoting the GDUC are put forward.

2. Theories and Methods

2.1. Connotation and Definition of GDUC

There are various concepts associated with green development, such as green economy, green growth, and low-carbon development. While these concepts differ in formulations, they all share the fundamental objective of coordinating and integrating circular economy development with ecological environment protection [25]. OECD [26] summarizes green growth as the growth of low-carbon, ecological and circular economy through technological means. In the context of China, the concept of green development is viewed as a successor to the sustainable development theory and represents the second generation of sustainable development, which pays more attention to the necessity for green transformation rather than supplementary adjustments to the existing development model [27,28].

Urban construction can be broadly classified into physical and non-physical construction, with the former also known as urban engineering construction that encompasses housing construction and varieties of infrastructure constructions [29]. Urban engineering construction is regarded as a comprehensive production activity aimed to transform the living environment, establish material facilities, and generate economic value. It shapes the physical appearance of urban structures, reflects the economic production process of the construction industry, and involves the majority of resource consumption and waste generation. Thus, evaluating its green development status is critical for achieving a green city, and the adoption of sustainable practices in its production process is essential for effective industrial transformation.

Therefore, introducing the concept of green development into the field of engineer construction, this paper defines GDUC as a model that incorporates green building technology, drives the transformation of the engineering construction field, and enables the coordination of engineering construction activities with the principles of green economy and the social-ecological environment in urban systems. Compared to other extended concepts of green development, GDUC has multiple characteristics in terms of materiality, sociality, and economy aspects, so this article intends to study it from the perspectives of output status and production efficiency.

2.2. Status Measurement of GDUC

The study of status measurement involves quantitatively describing the development situation and characterizing the development state of realistic phenomena or things. Given the complexity of urban engineering construction, measuring its green development status through a single or a small number of indicators is a challenge. Thus, a comprehensive evaluation model is necessary to synthesize multiple indicators and objectively assess the results.

2.2.1. Construction of the Comprehensive Evaluation Criteria System

In accordance with the principles of purposiveness, completeness, operability, hierarchy, and scientificity of index design, this paper has formulated an evaluation criteria system for the status of GDUC. The system consists of 32 specific indicators, which are grouped into three system layers (green construction, green growth, and green management), comprising a total of eight factor layers.

Green construction is a production activity with complete and green characteristics, which is divided into housing construction, traffic construction, and municipal construction in this paper, referring to the research of Liang Q and Li Xu et al. [29,30]. For housing construction indicators, per capita construction area and per capita completed area represent the development of housing construction scale; pre-fabricated construction area represents the promotion of green construction technology to construction development; and per capita construction waste output embodies the effort to save resources. The traffic construction mainly includes the construction of urban roads and rail transit, which reflects the spatial intensity of city, here represented by three indicators: per capita road area, road network density, and rail transit line length. Municipal construction refers to the construction of urban municipal utilities which improve the basic facilities for urban life. According to the principle, the water penetration rate, gas penetration rate, water supply pipeline density, and drainage pipeline density of built-up areas are selected.

Green growth signifies a novel economic production mode which is the desired outcome of various economic activities, aiming at improving economic production efficiency while reducing energy consumption. Referring to the study of Ye H et al. [31], this part is divided into comprehensive output and energy consumption based on the input and output perspective. Comprehensive output refers to the economic benefits of construction activities under the constraints of energy, resources, and labor. It includes indicators such as the energy output rate, construction land output rate, construction labor productivity, and per capita completion output value. Energy consumption takes into account the overall energy consumption of the region as well as the energy consumption of construction activities. The former includes indicators such as per capita energy consumption and total energy consumption growth rate. The latter includes the energy consumption intensity of construction activities and the CO₂ emissions intensity of construction activities.

Green management reflects the development state of each participant in construction activities from a social perspective, expressed through environment capacity, resource consumption, and capital input [32]. The environment capacity is considered from two aspects. The first aspect is capacity of land resources for population and construction, including population density, per capita urban construction land area and urbanization level. The second aspect is the capacity of green urban space, including per capita park green space area, green coverage rate of built-up area, and green space rate of built-up area. The resource consumption indicators are reflected in the utilization of daily resource by urban residents, such as sewage treatment plant centralized treatment rate, domestic waste harmless treatment rate, per capita water consumption, and per capita domestic waste emissions. Asset investment has played a role in promoting and maintaining urban construction activities, so indicators such as urban municipal public facility construction investment, real estate development investment, and urban environmental infrastructure construction investment are selected.

The comprehensive evaluation criteria system of GDUC is shown in

Table 1. The comprehensive evaluation criteria system of GDUC.

System Layer	Factor Layer	Indicator Layer	Unit	Indicator Direction
Green Construction (A1)	Housing Construction (B1)	Per capita construction area (X1)	m2/person	+
		Per capita completion area (X2)	m2/person	+
		Per capita construction waste (X3)	t/person	−
		New prefabricated building area (X4)	%	+
	Traffic Construction (B2)	Per capita road area (X5)	m2/person	+
		Road network density (X6)	km/km2	+
		Rail transit line length (X7)	km	+
	Municipal Construction (B3)	Water penetration rate (X8)	%	+
		Gas penetration rate (X9)	%	+
		Water supply pipeline density (X10)	km/km2	+
		Drainage pipeline density (X11)	km/km2	+
Green Growth (A2)	Comprehensive Output (B4)	Energy output rate (X12)	yuan/kgce	+
		Construction land output rate (X13)	yuan/m2	+
		Construction labor productivity (X14)	yuan/person	+
		Per capita completion output value (X15)	yuan/person	+
	Energy Consumption (B5)	Per capita energy consumption (X16)	kgce/person	−
		Total energy consumption growth rate (X17)	%	−
		Energy intensity of construction activities (X18)	kgce/m2	−
		CO2 emission intensity of construction activities (X19)	kgCO2/m2	−
Green Management (A3)	Environment Capacity (B6)	Population density (X20)	person/km2	+
		Per capita urban construction land area (X21)	m2/person	+
		Urbanization level (X22)	%	+
		Per capita park green area (X23)	m2/person	+
		Green coverage rate of built-up area (X24)	%	+
		Green space rate of built-up area (X25)	%	+
	Resource Consumption (B7)	Sewage treatment plant centralized treatment rate (X26)	%	+
		Domestic waste harmless treatment rate (X27)	%	+
		Per capita water consumption (X28)	L/person/day	−
		Per capita domestic waste emissions (X29)	kg/person/day	−
	Asset Investment (B8)	Urban municipal public facilities construction investment (X30)	billion yuan	+
		Real estate development investment (X31)	billion yuan	+
		Urban environmental infrastructure construction investment (X32)	billion yuan	+

The + represents a positive direction and − represents a negative direction in this paper.

. The corresponding data required for the status measurement is obtained from the China Statistical Yearbook on Construction, the China Urban Construction Statistical Yearbook, and the China Statistical Yearbook from 2011 to 2020.

Table 2. Descriptive statistical results for status measurement indicators.

Variable	Sample Size	Mean	Standard Deviation	Min	Max
X1 1	70	252.31	59.64	134.3	433.9
X2 1	70	85.58	18.96	46.9	146.6
X3 1	70	140.08	32.7	77.89	230.39
X4 1	70	1053.34	1744.02	0	8167.8
X5 2	70	16.67	5.98	4.04	25.62
X6 2	70	7.31	1.62	4.3	10.64
X7 2	70	208.43	240.98	0	834.75
X8 2	70	98.91	1.18	95.68	100
X9 2	70	97.36	2.8	88.45	100
X10 2	70	17.63	7.6	8.97	37.69
X11 2	70	12.57	3.36	7.74	21
X12 3	70	528.1	1243.26	11.84	3827.86
X13 3	70	6859	16,000.12	660	101,200
X14 1	70	329,559.71	175,048.85	1.65	752,079
X15 1	70	184,458.64	90,734.2	4.37	406,081
X16 3	70	3017.97	972.73	1543.71	4816.86
X17 3	70	1.12	12.74	−100	10.12
X18 1	70	3.07	1.84	0.36	9.23
X19 1	70	8.55	4.61	1.42	23.11
X20 2	70	2968.45	926.26	1741	4822
X21 2	70	136.61	24.4	78.35	176.62
X22 2	70	62.22	12.95	44.8	89.6
X23 2	70	12.09	2.4	7.01	15.34
X24 2	70	40.85	2.61	36.24	46.81
X25 2	70	36.99	2.93	32.55	43.35
X26 2	70	88.03	6.77	65.04	96.95
X27 2	70	95.64	8.08	61.02	100
X28 2	70	196.56	16.27	165.45	220.69
X29 2	70	1.37	0.43	0.69	2.49
X30 2	70	90.45	46.02	29.48	215.36
X31 3	70	484.13	284.34	86.7	1317.13
X32 3	70	24.38	11.91	5.817	57.931

¹ Form the China Statistical Yearbook on Construction. ² Form the China Urban Construction Statistical Yearbook. ³ Form the China Statistical Yearbook.

shows the specific data sources and descriptive statistics for all indicators, with 70 valid observations obtained for each variable.

2.2.2. Comprehensive Evaluation Method

There are two types of evaluation methods based on the method of weighting assignment. The first is the subjective weighting method, which includes the Delphi method, analytic hierarchy process, or fuzzy comprehensive evaluation. The second is the objective weighting method, which includes principal component analysis, entropy weight method, or comprehensive index method [33]. While the subjective weighting method requires collecting initial data through questionnaire surveys, an excessive number of indicators can complicate questionnaire design and question selection. Therefore, it may not be suitable for evaluating and analyzing a large number of indicators. Given the numerous constituent factors and indicators involved in GDUC, the objective weighting model that combines the entropy weight method and comprehensive index method is more appropriate for analysis [34].

The entropy weight method is a calculation method that utilizes the information provided by the entropy value to determine the weight of each indicator. The entropy value represents the level of uncertainty in the information system, with higher entropy values indicating greater uncertainty and lower weights assigned to the corresponding indicators [35]. On the other hand, the composite index method offers a comprehensive and thorough evaluation of the research subject by combining multiple independent variables or indicators into a single index. Each indicator represents a distinct aspect of the object, and a composite index is obtained by calculating a weighted average of all indicators [36]. A higher value of the composite index indicates a more favorable state of development.

Based on the indicator data, the model calculation consists of three stages. The first stage is the standardized matrix processing stage. For each region, the years are taken as the evaluation sample m, and the indicators are taken as variables n to create the original data matrix X.

$$X={\left(\begin{array}{cccc}{x}_{11}& x_{12}& \dots & x_{1n}\\ x_{21}& x_{22}& \cdots & x_{2n}\\ ⋮& ⋮& \ddots & ⋮\\ x_{m1}& x_{m2}& \cdots & x_{mn}\end{array}\right)}_{m\times n}$$

(1)

Step 1: use standardized method to process matrix X to obtain the standardized matrix R.

$$r_{ij}=\frac{x_{ij}-min(x_j)}{max(x_j)-min(x_j)}$$

(2)

$$r_{ij}=\frac{max(x_j)-x_{ij}}{max(x_j)-min(x_j)}$$

(3)

The second stage is weight value calculation based on entropy weight method.

Step 2: calculate the proportion of each object under the j-th variable.

$$f_{ij}=\frac{r_{ij}}{{\displaystyle \sum _{i=1}^m{r}_{ij}}}$$

(4)

Step 3: calculate the information entropy H_j of the j-th variable.

$$H_j=-k{\displaystyle \sum _{i=1}^m{f}_{ij}}\cdot \ln f_{ij},k=\frac{1}{\ln m}$$

(5)

Step 4: calculate the weight value w_j of j-th variable.

$$w_j=\frac{1-H_j}{{\displaystyle \sum _{j=1}^n(1-H_j)}}=\frac{1-H_j}{n-{\displaystyle \sum _{j=1}^n(H_j)}}$$

(6)

The third stage is the calculation of the composite index, which is divided into green construction (GC), green growth (GG), and green management (GM) to calculate the sub-index.

Step 5: calculate the composite index.

$$\begin{array}{c}{S}_i=S_{GC}+S_{GG}+S_{GM}={\displaystyle \sum _{j=1}^{n_{GC}}{w}_j\times r_{ij}^{\prime }}+{\displaystyle \sum _{j=1}^{n_{GG}}{w}_j\times r_{ij}^{\prime }}+{\displaystyle \sum _{j=1}^{n_{GM}}{w}_j\times r_{ij}^{\prime }}\\ n_{GC}+n_{GG}+n_{GM}=n\end{array}$$

(7)

Through the application of the comprehensive evaluation model, we can determine the weight of each indicator, as well as the composite value of the sub-index and the overall index. By conducting a thorough analysis in both temporal and spatial dimensions, we can effectively measure the status of GDUC.

2.3. Efficiency Measurement of GDUC

In addition to status measurement, efficiency measurement is also a commonly evaluation method in the research of green development evaluation. However, the terms of productivity and efficiency are often confused with each other in research papers, despite their similar meanings in statistics [37]. Productivity is commonly defined as the ratio of production to input, which reflects the outcomes of production. In contrast, efficiency is a measure of the ability to mobilize and utilize resource elements, reflecting the production process [38]. Both productivity and efficiency calculations depend on the amount of input and output. The inputs often include capital, labor, energy, and resources, while outputs often include productive value, products, waste emissions, and carbon emissions. The efficiency of all outputs under fixed input is named as comprehensive technical efficiency, while the efficiency of a single input or output is referred to partial efficiency, such as energy efficiency, ecological efficiency, and carbon emission efficiency [39].

In this paper, we apply the super-efficiency slacks-based measure (SBM) model to evaluate the efficiency of GDUC, incorporating both solid waste and carbon emissions as the undesirable outputs. The model is used to determine the overall efficiency of the green development process, which is referred to as green development efficiency or green production efficiency.

2.3.1. Selection and Analysis of Input–Output Indicator

According to the principles of purposiveness, completeness, and operability, this paper selects four input elements of capital, labor, material, and energy, as well as four output elements of economy, construction, solid waste emissions, and CO₂ emissions to form the input–output indicator system. The output indicators are divided into desired outputs in economy and construction, and undesired outputs in solid waste and CO₂ emissions, according to the production purpose.

Table 3. Input–output indicator system.

System Layers	Factor Layers	Indicator Contents	Units
Input indicators	Capital input	Investment for real estate development and urban municipal public facilities construction	billion yuan
	Labor input	Persons engaged in construction activities	person
	Material input	Building material consumption of steel, aluminum, wood and cement	1021 sej
	Energy input	Energy consumption during engineering construction	107 kgce
Output indicators	Economy output	Total output value of construction industry	billion yuan
	Construction output	Completion building area of construction	104 m3
	Solid waste emissions	Waste of engineering, excavation, demolition, decoration and road	104 t
	CO2 emissions	CO2 from building materials production and construction production	104 tCO2e

displays the indicator contents, and the essential data is sourced from the China Statistical Yearbook on Construction and the China Building Industry Yearbook from 2011 to 2020. The major statistical information of indicators is shown in

Table 4. Descriptive statistical results for input–output indicators.

Variable	Sample Size	Mean	Standard Deviation	Min	Max
Capital input 1	70	574.66	323.11	141.22	1514.63
Labor input 1	70	3,453,882	2,696,169	831,029	9,739,582
Material input 1 4	70	521.91	417.02	75.7	2295.41
Energy input 1	70	184.98	97.15	31.55	361.64
Economy output 1	70	1195.47	835.59	209.55	3525.16
Construction output	70	19415.9	16,178.15	2906.4	57,589
Solid waste emissions 1	70	49,501.05	40,605.58	9155.36	146,097.11
CO2 emissions 1	70	22,833.56	16,977.42	3135.96	85,981.59

¹ Form the China Statistical Yearbook on Construction. ⁴ Form the China Building Industry Yearbook.

.

Meanwhile, to analyze the role of various indicators in the production system, we can examine the flow of factors, as illustrated in Figure 2. The solid arrows indicate the actual direction of factor flows, whereby capital, labor, materials, and energy are fed into the production process as the four basic inputs. Through this process, the desired outputs of economic benefits and construction state are produced, along with the undesired outputs of solid waste and CO₂ emissions. In contrast, the dashed line indicates the potential direction of factor flows. The solid waste that has not been utilized or produced from construction forms construction waste, and meanwhile, the less recycling technology we use, the higher output rate the solid waste has. As for CO₂ emissions, there are two inflow directions of CO₂ emissions: from building materials production and from the construction site. Therefore, for reducing CO₂ emissions from the whole process, it is crucial to improve the technique of building materials and adopt the building method of industrialization or assembly.

2.3.2. Efficiency Calculation Based on DEA Method

DEA is a non-parametric method for evaluating the relative effectiveness of similar units with multiple inputs and outputs based on the principle of linear programming. It was firstly introduced in the 1970s and has since been expanded to several efficiency evaluation models [40]. Given the presence of undesired output in the indicator system, the paper employs the super-efficiency and undesirable SBM-DEA model for efficiency evaluation [41].Based on the data of input–output indicators, the calculation process of DEA model is as follows. Taking regions as the decision-making units (short for DMUs, count R), it includes m input indicators (denoted by X), n desired output indicators (denoted by Y), and h undesired output indicators (denoted by B) for each DMU. The formula of efficiency calculation can be expressed as Equation (8).

$$\begin{array}{c}{\theta }_k^{*}=\underset{\lambda ,S^{-},S^{+}}{\min }\frac{1-\frac{1}{m}{\displaystyle \sum _{i=1}^m\frac{S_i^{-}}{X_i^k}}}{1-\frac{1}{n+h}({\displaystyle \sum _{j=1}^n\frac{S_j^{+}}{Y_j^k}+\sum _{p=1}^h\frac{S_p^{-}}{B_p^k})}}\\ s.t.{\displaystyle \sum _{r=1}^R{\lambda }_r{X}_i^r}+S_i^{-}\le X_i^k,i=1,2,\dots ,m\\ {\displaystyle \sum _{r=1,r\ne k}^R{\lambda }_r{Y}_j^r}+S_j^{+}\ge Y_j^k,j=1,2,\dots ,n\\ {\displaystyle \sum _{r=1,r\ne k}^R{\lambda }_r{B}_p^h}+S_p^{-}\le B_p^k,p=1,2,\dots ,h\\ {\displaystyle \sum _{r=1}^R{\lambda }_r=1}\\ S_i^{-},S_j^{+},{\lambda }_r\ge 0,r=1,2,\dots ,R;i=1,2,\dots ,m;j=1,2,\dots ,n\end{array}$$

(8)

In Equation (8), ${\theta }_k^{*}$ represents the value of relative efficiency obtained, $\lambda$ represents the wight value of DMUs, and $S^{+}\mathrm{o}\mathrm{r}{S}^{-}$ represents the slack variable of indicators.

Compared with the comprehensive evaluation model, the DEA-SBM model offers a simpler procedure that does not require standardization of raw indicator data. It utilizes a minimal number of input and output indicators to generate efficiency evaluation results. On the other hand, the use of the super-efficiency and undesirable SBM model enables accurate consideration of the impact of solid waste and carbon emissions in urban engineering and construction activities. Additionally, the super-efficiency feature of the SBM model facilitates the comparison of efficiency among DMUs. And a higher composite value obtained through this model calculation signifies a higher level of efficiency in green development.

3. Results

3.1. Analysis of Status Measurement of GDUC

3.1.1. Determination of Indicator Weight

Using the entropy weight method, the weight values of 32 indicators in the lowest layer were calculated from the entire sample space, as illustrated in Figure 3. Generally, the weight values are distributed in the range from 1% to 10%, with the majority falling between 2% and 4%. Indicators in the high numerical stage exhibit a small coefficient of variation and consequently possess lower weight values, such as the sewage treatment plant centralized treatment rate (X26), domestic waste harmless treatment rate (X27), water penetration rate (X8), and gas penetration rate (X9). In contrast, specific indicators related to green city development show a rapid increase and higher weight value, such as the new prefabricated building area (X4), road network density (X6), and rail transit line length (X7).

The weights of the factor layer can be obtained by summing the weights of the indicator layer, sorted by size as environment capacity (B6), housing construction (B1), traffic construction (B2), comprehensive output (B4), energy consumption (B5), municipal construction (B3), resource consumption (B7), and asset investment (B8). These weight values represent the importance of the factor in the evaluation.

3.1.2. Calculation of Comprehensive Status Index

Based on the calculation of weight, the comprehensive evaluation method was used to calculate the status index of GDUC for the overall and each individual region with the MLRYR. The results showed that from 2011 to 2020 in the MLRYR, the status of GDUC displayed an consistent upward trend, which can be divided into three stages. The comprehensive status index from 2011 to 2014 ranged from 0.143 to 0.455, signifying a stage of uniform growth. The index from 2014 to 2019 ranged from 0.455 to 0.675, which was a stage of gradual and slow growth. Finally, the index from 2019 to 2020 ranged from 0.675 to 0.84, indicating a stage of rapid growth.

As for the sub-index, in the initial stage, green management developed rapidly and played a promoting role. During the second stage, green construction and green management decreased initially and then increased. However, the construction volume decreased slightly, slowing down the growth rate of the comprehensive status index during this period. The third stage witnessed significant improvement in all aspects of green construction, green growth, and green management. These improvements can be attributed to policy promotion, resulting in the comprehensive status index reaching a maximum value of 0.84. The calculation results are presented in Figure 4.

The status comparison of GDUC in various regions over the past decade is shown in Figure 5. On the whole, the curve trend in each region followed a similar pattern to the overall change situation. Most regions have kept increasing year by year, except for Shanghai in the period 2016–2018 and Jiangxi in the period 2013–2014 decreasing slightly. The green development speed of Jiangsu and Anhui has reached 0.5 per year, while the speed of other regions was also above 0.45 per year. By 2020, the status index of GDUC in each region had peaked. Specifically, the status index of Jiangsu and Hunan exceeded 0.8, while the status index of Zhejiang, Anhui, Jiangxi, and Hubei was between 0.75 and 0.8. Shanghai had a lowest status index, standing at just 0.692.

In short, the urbanization construction in MLRYR has managed to maintain its green development status while pursuing rapid growth. The development trend can be observed in distinct stages. With the in-depth promotion of low-carbon and green city policies after 2020, it is expected that the status of GDUC will be further improved.

3.2. Analysis of Efficiency Measurement of GDUC

Using the SBM model, the efficiency values of GDUC in MLRYR were calculated and decomposed into technical efficiency and scale efficiency, as shown in Figure 6. Technical efficiency signifies the effect of management and technical level, whereas scale efficiency reflects the impact of scale change. Similarly, the curves can be divided into two stages based on the evolution. In the initial stage (2011–2014), technical efficiency continuously improved until the technology became effective (TE > 1), while scale efficiency decreased continuously due to the smaller production scale. This caused the efficiency to increase from 0.454 to 0.607. In the subsequent stage (2014 to 2020), technical efficiency remained flat, scale efficiency had a slight increase, and green development efficiency increased slowly under the factor actions. The maximum efficiency value was 0.713 in 2020, but it still remained in the state of inefficient DEA.

In general, improving the efficiency value of GDUC requires development of both production scale and technology application. Production scale should align with production capacity to avoid wastage of production factors. On the other hand, the more technologies applied in engineering construction, the less undesired outputs are emitted, resulting in an enhancement of production efficiency.

Calculate the efficiency value of GDUC in each region of MLRYR, as shown in Figure 7. The research indicated that the efficiency value of GDUC in Anhui and Hunan consistently ranged between 0.4 and 0.6, which was significantly lower than other regions and responsible for the low production efficiency. During the period of 2013–2015, there was a noticeable decline of efficiency observed in Shanghai, Zhejiang, and Hubei, which can be attributed to the reduction in investment in material and energy in Shanghai and Hubei and an increase in solid waste and CO₂ emissions in Zhejiang. In 2014, the efficiency value reached its highest point at 1.246, indicating that the factor utilization was at its best. The efficiency value of Jiangsu and Hunan has gradually increased over the past decade.

Overall, the efficiency values of GDUC, which reflect the degree of utilization of various production factors, vary significantly among different regions. Improved efficiency is achieved when expected outputs increase and unexpected outputs decrease under the same input.

3.3. Level Evaluation of GDUC Based on Status and Efficiency

Status and efficiency are the primary means used to evaluate green development and quantitatively describe the GDUC from two dimensions of construction achievements and construction process. In this study, we focused on the MLRYR region and employed a comprehensive evaluation model to derive the comprehensive status indexes of GDUC and a SBM model to calculate the efficiency values of GDUC. By considering the overall region as the control group, an average status value of 0.418 was calculated to distinguish between ‘poor status’ and ‘excellent status’, while an average efficiency value of 0.615 was calculated to classify ‘low efficiency’ and ‘high efficiency’. Subsequently, the comprehensive level of GDUC in each region of MLRYR was evaluated, and the results are presented in Figure 8 and

Table 5. Measurement of status and efficiency of GDUC in MLRYR.

Year	MLRYR		Shanghai		Jiangsu		Zhejiang		Anhui		Jiangxi		Hubei		Hunan
	Status	Efficiency	Status	Efficiency	Status	Efficiency	Status	Efficiency	Status	Efficiency	Status	Efficiency	Status	Efficiency	Status	Efficiency
2011	0.14	0.45	0.21	1.00	0.16	0.56	0.23	1.02	0.16	0.42	0.23	0.63	0.13	0.53	0.17	0.43
2012	0.26	0.53	0.31	1.00	0.24	0.69	0.32	0.82	0.29	0.47	0.33	0.83	0.26	1.02	0.25	0.46
2013	0.36	0.58	0.38	0.97	0.38	0.84	0.4	0.83	0.38	0.47	0.32	1.01	0.34	1.00	0.35	0.49
2014	0.45	0.61	0.46	0.72	0.46	0.89	0.45	0.8	0.46	0.49	0.31	1.25	0.42	0.63	0.37	0.50
2015	0.46	0.63	0.51	0.79	0.49	1.03	0.46	1.00	0.49	0.55	0.37	0.78	0.39	0.8	0.45	0.54
2016	0.51	0.63	0.49	1.02	0.54	0.98	0.54	0.98	0.53	0.53	0.37	1.02	0.49	0.7	0.53	0.55
2017	0.55	0.66	0.57	0.9	0.56	1.03	0.48	1.00	0.58	0.5	0.5	1.00	0.53	0.82	0.54	0.57
2018	0.61	0.66	0.52	1.03	0.64	1.00	0.6	0.49	0.62	0.52	0.65	1.00	0.61	1.03	0.56	0.57
2019	0.68	0.68	0.61	0.64	0.7	1.02	0.66	0.48	0.73	0.44	0.69	1.01	0.67	1.04	0.64	0.58
2020	0.84	0.71	0.69	1.02	0.82	1.02	0.76	0.49	0.77	0.54	0.8	1.03	0.76	1.03	0.84	0.63
avg	0.49	0.61	0.48	0.91	0.5	0.91	0.49	0.79	0.5	0.49	0.46	0.96	0.46	0.86	0.47	0.53

.

The research revealed four levels of GDUC. With the dividing year set in 2015, ‘poor status—low efficiency’ was mainly observed in the early development of Hunan and Anhui. ‘Excellent status—low efficiency’ appeared during the later development stage of Hunan, Anhui, Zhejiang, and the early development stage of Hubei. In contrast, ‘poor status—high efficiency’ was seen in the early development stages of Jiangxi, Zhejiang, Jiangsu, and Shanghai, while ‘excellent status—high efficiency’ was observed during the later stages of development in Hubei, Jiangxi, Jiangsu, and Shanghai. Overall, the GDUC in MLRYR evolved from ‘poor status—low efficiency’ to ‘excellent status—high efficiency’, indicating significant progress in green development. Moreover, substantial disparities were found in the level of GDUC among different regions, reflecting variations in their urban construction stages.

4. Discussion

Combining Figure 4 with Figure 6, the overall situation of GDUC in MLRYR was analyzed in terms of time scale. The study revealed that there was an upward trend in both the overall status and efficiency of GDUC between 2011 and 2020. The status index increased from 0.143 to 0.840, while the efficiency value increased from 0.454 to 0.713. Despite their distinct calculation principles, the status index displayed a more pronounced phase development and rapid increase compared to the efficiency value. The reason behind this outcome is that the green development status, as the result description of urban engineering construction, primarily selects the aggregate indicators to demonstrate the overall scale and level of urban construction [42]. Consequently, as the accumulation of urban engineering construction increases, the green development status increases correspondingly. On the other hand, green development efficiency describes the process of urban engineering construction taking into account the comprehensive relationship between input and output indicators, which increases with the higher utilization of production factors [43]. Therefore, improving green development status is relatively simpler and more feasible than improving efficiency. However, the analysis of efficiency can directly reflect the development of technical progress and application level, which is also an essential factor influencing the improvement of GDUC. In addition, the green development status revealed a three-stage distribution, which can be attributed to the influence brought by policy changes. With the policies of National Plan on New Urbanization Construction in 2014 and the Guidelines on Accelerating Ecological Civilization Construction in 2015 [44], the objective of urban construction has shifted from scale expansion to green ecological direction, and the reduction of construction volume has slowed down the growth rate of the status index but further advanced the green development. After 2020, with the implementation of policies such as Carbon Peaking and Carbon Neutrality Goals, as well as the promotion of Green Development of Urban and Rural Construction, a comprehensive construction method emphasizing low-carbon, low-consumption, high-efficiency, and environmentally friendly construction has been proposed and become the key to green construction transformation, and various regions have once again set off a wave of green development in China [45].

Combined with Figure 5 and Figure 7, analyze the situation of GDUC in each region of MLRYR from the spatial scale. According to geographical location, the MLRYR can be divided into the Yangtze River Delta (including Shanghai, Jiangsu, Zhejiang and Anhui) and the Middle Reaches of Yangtze River (including Jiangxi, Hubei and Hunan). Based on the data in Table 5, the average status indexes in the Yangtze River Delta ranged from 0.48 to 0.5, slightly higher than the average status indexes in the Middle Reaches of Yangtze River which were in the range of 0.46 to 0.47, showing that the Yangtze River Delta had an earlier urban development and construction foundation [46]. In general, the average status indexes were slightly different, but there was significant variability in the average efficiency values of GDUC. Specifically, the average efficiency values of Shanghai, Jiangsu, and Jiangxi reached above 0.9, and Zhejiang and Hubei exhibited average efficiency values around 0.8, while Hunan and Anhui had notably lower efficiency values than other regions, only around 0.5. The reason may be that this paper considered the impact from solid waste emissions and carbon emissions in the calculation of efficiency compared to other models, and these two indicators are closely related to the recycling technologies and low-carbon technologies [47]. However, there were great differences in the R&D and application of new construction technologies in different regions, which required a large amount of capital investment [48,49]. Although the efficiency of GDUC in each region has improved by the policy incentives, it was still not enough to compensate for the disadvantages caused by the technical level, which created the differences in efficiency of GDUC between regions. Therefore, enhancing information sharing and experience exchange and supporting technology transfer and innovation cooperation have become unavoidable topics for GDUC within regions [50].

Although the calculation models for green development status and efficiency yield different results and represent distinct meanings, both serve as crucial evaluation indices for assessing the progress of GDUC. The status index provides an overview of the scale and level of green urban construction, while the efficiency value reflects the productivity of such endeavors. These comprehensive indices demonstrate diverse patterns of development across time and space within the study area. Through careful analysis and discussion, this paper identifies two main factors contributing to these variations: policy incentives and changes; and technological advancements and application. Policy incentives, as external factors, drove the overall development of green status in the region. Due to policy changes occurring at different points in time, the green status exhibited a phased development over time. On the other hand, technological progress and application acted as internal factors, with industry production scale and technological levels influencing green development efficiency. While policy incentives have led to improved efficiency in GDUC across regions, they have not fully compensated for differences in technological capabilities. Consequently, variations in technology levels result in spatial differentials in green development efficiency.

In conclusion, the attainment of green city goals necessitates the synchronized development of green development status and efficiency, considering both external and internal factors. It is essential to align policy incentives, technological advancements, and application to achieve a harmonious and sustainable green city.

5. Conclusions

In the study, we adopt the comprehensive evaluation model and super-efficiency SBM Model to measure the status and efficiency of GDUC in the MLRYR from 2011 to 2020 and make a comprehensive level evaluation of GDUC based on the results. The main conclusions obtained are as follows:

(1)

The status of GDUC in the MLRYR exhibits an overall upward tendency over the years, characterized by obvious stages of evaluation. Among these stages, green construction has the greatest impart, green management has the second impart, and green growth has the least impart. The status curve of GDUC in each region is similar to the overall curve, and all the status indexes reach the maximum value in 2020, with environment capacity, housing construction, and traffic construction as the main accounting factors.

(2)

The efficiency of GDUC in the MLRYR has a tendency to gradually and slowly increase, in which technical efficiency initially increases and then stabilizes at 1, while scale efficiency firstly decreases and then slowly increases. As the reflection of the utilization of various factors, the efficiency of GDUC has significant variability among the whole region, and the efficiency values of Anhui and Hunan are much lower than those of other regions. Therefore, strengthening the application of technology and maintaining a moderate scale are important means to increase desired output, reduce undesired output, and improve the efficiency of GDUC.

(3)

By integrating the assessments of status and efficiency, the level of GDUC in the MLRYR has evolved from ‘bad status—low efficiency’ to ‘excellent status—high efficiency’. However, the level of GDUC varies greatly among regions, which is affected by geopolitical factors and reflects the different stages of development in urban engineering construction across the various regions.

After analysis, this paper finds that both the status and efficiency of GDUC have been developing year by year in the MLRYR. However, it has also exposed the problems of incomplete development status, ineffective development efficiency, and unbalanced development level. Thus, we propose some corresponding countermeasures to promote the realization of GDUC in China from three perspectives: urban construction, economic production, and social communication.

(1)

Firstly, our recommendations are to complete urban construction facilities and create a green construction model. The state of urban engineering construction should focus on completeness and greenness, while prefect urban construction facilities are the precondition for the development of green construction. Through urban construction planning, we should guarantee a reasonable and moderate urban development intensity, improve the systematic construction of transportation facility, and develop a new green building system. The reasonable urban development intensity should consider factors such as population density, land using efficiency, and transportation connectivity. The systematic construction of transportation facility entails developing an efficient transportation network that prioritizes public transit, cycling lanes, and pedestrian-friendly infrastructure. Furthermore, the new green building system requires adopting green design and construction practices, including green building materials and techniques to reduce the carbon footprint of urban engineering projects. In addition to infrastructure development, the quality of municipal projects such as urban water supply and drainage, sewage and waste treatment, and greening should be enhanced to provide more suitable and green activity places for production and daily life.

(2)

Secondly, it is recommended to implement the techniques and management methods of green building to enhance green production efficiency. Aiming at low carbon, low consumption, and high efficiency during economic production, we must curb the undesired outputs such as solid waste emissions, CO₂ emissions, and engineering pollution. This can be achieved through the adoption of sustainable construction practices, including waste management strategies, energy-efficient technologies, and green building materials. The integrated application of green building technology in engineering construction activities can significantly reduce the environmental impact and enhance the overall efficiency of the construction process. On the other hand, from the life cycle perspective, it is essential to connect upstream and downstream enterprises within the industry, including planning, design, production, transportation, construction, and management. By fostering collaboration and communication among different stakeholders, the industry can improve the quality and competitiveness of production. Implementing green practices and sustainable standards across the entire industry chain can lead to the transformation of the construction industry towards a more environmentally friendly and sustainable direction.

(3)

Thirdly, it is proposed to strengthen social exchange and cooperation to create a harmonious development environment for urban construction. We should closely follow the policies about the pilot sites of green building and green city released by ministries and government agencies by staying informed, aligning with green standards, and benefiting from available incentives and support. Then, increasing the service support for green financial projects can facilitate the financing and investment in sustainable urban construction development. Further, the establishment of an exchange cooperation platform of industry–academia–research can accelerate the R&D of industrialization technology and low-carbon technology. This platform will encourage collaboration between industry practitioners, academic institutions, and research organizations, fostering the innovation of sustainable practices in urban construction. Additionally, it is necessary to promote the resource flowing and information sharing, deepen the labor division and regional collaboration, and enhance the linkage effect and overall synergistic development of urban construction.

As mentioned above, this paper presents policy recommendations to promote the GDUC in China, focusing on three aspects: urban construction, economic production, and social exchange. However, the implementation of these policies may encounter constraints originating from various sources. Firstly, financial limitations pose a significant challenge as substantial investments are required for the adoption of green building practices and infrastructure upgrades. Limited funding and budgetary constraints may hinder the adoption of sustainable technologies, materials, and large-scale infrastructure improvements. Secondly, resistance to change within the construction industry, including contractors, developers, and professionals, could impede the advancement of new green practices. Factors such as limited awareness, concerns about increased costs, and disruptions to established construction processes may contribute to this resistance. Additionally, conflicting regulations at different levels of governance and incomplete regulatory frameworks may pose obstacles to achieving green development goals, despite the alignment of green city building with the trajectory of social development.

This paper evaluates the green development level of urban engineering construction in the MLRYR by considering both the state and efficiency dimensions. Based on this evaluation, targeted policy recommendations are proposed to promote green urban development practices in the region. Additionally, this study serves as a valuable resource by providing references and tools for the long-term monitoring of GDUC in the area. Furthermore, it enables comparative analysis with other national regions or cities facing similar challenges, offering valuable insights into best practices, lessons learned, and innovative strategies from around the world.