The Efficiency of the Chinese Prefabricated Building Industry and Its Influencing Factors: An Empirical Study

Abstract

China is a world leader in capital construction. In the construction field, the shift toward prefabricated construction has become an important path for industrial transformation. This paper refers to the development of the prefabricated building industry in China, and uses input and output perspectives to examine its efficiency. It builds a data envelopment analysis model to evaluate the efficiency of the prefabricated building industry in China at both the micro and macro levels, and uses the Tobit model to empirically analyze the factors that influence this industry’s efficiency. It finds that the country’s prefabricated building industry has a moderate micro-level efficiency. This means that it is necessary to further rationalize industrial planning; strengthen technological innovation; and improve standardization, mechanization, and automation levels. At the macro level, China’s prefabricated buildings have a low industrial efficiency and remain at the initial stage of industrial development. A series of problems, such as small industrial scale and unsound policies, are restricting the industry’s rapid and efficient development. We propose several countermeasures and suggestions for the (micro- and macro-level) sustainable development of the prefabricated building industry in China, and anticipate that this will have implications for this industry’s worldwide development.

1. Introduction

As an important material production sector in the Chinese national economy, the building industry is closely related to the economic development of the country as a whole and the improvement of people’s lives (in 2016, the General Office of the State Council issued the “Guiding Opinions of the General Office of the State Council on Vigorously Developing Prefabricated Buildings”. The growth stage and rapid expansion of the Chinese prefabricated building industry started soon after. The “Guiding Opinions of the General Office of the State Council on Promoting the Sustainable and Sound Development of the Building Industry” and the “Several Opinions on Accelerating the Development of New-type Building Industrialization”, which were both issued in 2017, emphasize that the country should improve the professional knowledge and technical level of professional technicians in the application field of building industrialization, train talents in accordance with the development of new-type building industrialization and constantly perfect relevant technical systems: these changes, it suggests, will ensure the future development of the prefabricated building industry.). In addition to rapidly developing, the building industry is influencing national economic growth in an increasingly significant way. However, China’s building industry is facing a series of problems, including labor shortages, severe pollution, poor technical systems, inadequate technological innovation and poor international competitiveness [1,2,3]. These problems are caused by the traditional construction method applied by the construction industry in the construction process, which scatters building material and generates a large amount of dust, resulting in environmental pollution. The accelerated pace of the country’s rural revitalization strategy and the increase in employment opportunities for villagers has increased the problem of aging construction workers, and the number of new young construction workers has dropped significantly, resulting in a labor shortage in the country’s industry. The overall technical content of the country’s construction industry is low, and it is still labor-intensive, rather than technology-intensive [4]. The formation of a high-quality development system framework and apparent improvements in the industrialization, digitalization, and intelligence levels of buildings have contributed to the rapid development of the prefabricated building industry and the effective alleviation of many problems that affect the current stage. The emergence of prefabricated buildings is driven by technological innovation in construction work. In relying on the industrial production of high-quality prefabricated units, most cast-in-place work in traditional building construction is now being completed at factories, before prefabricated units are assembled onsite by mechanized construction [5]. This helps to achieve sustainable development by integrating the completely industrial chain, increasing productivity and reducing labor demand and energy consumption [6,7]. This study defines productivity according to a prefabricated component factory and construction productivity.

Increased urbanization in the country created further demand for new buildings. A booming market and favorable policies caused the prefabricated building industry to rapidly expand. In 2016 alone, the market size of China’s prefabricated building industry achieved year-on-year growth of 392%, and the cumulative area of new prefabricated buildings increased by 57%. Technological upgrading, management model improvement, and structural optimization all helped to improve enterprises’ total factor productivity [8].

In 2016, new prefabricated buildings accounted for about 5% of the total area of new buildings in China. However, in the period 2017–2020, this proportion surged from 6.5% to 20.5 percent, and the market size expanded rapidly from 283.9 to 1227.7 billion yuan. However, when compared to the penetration of prefabricated buildings in developed countries (90% in the US and Japan, and 85% in France), the penetration rate of prefabricated buildings in China has the potential to increase significantly [9] (by 2020, the cumulative area of new prefabricated buildings in China had reached 630 million m²—with an annual growth of 50%, prefabricated buildings accounted for about 20.5% of the total area of new buildings [10]. The number of enterprises related to prefabricated buildings (hereafter referred to as “related enterprises”) has also rapidly increased.).

By 2020, the number of related enterprises in China had exceeded 14,000. However, the costs of prefabricated buildings were still higher than those of traditional cast-in-place buildings, mainly because of the low-capacity utilization rate of prefabricated component factories, single business patterns, and the scattered industrial distribution of related enterprises [10]. This ran counter to the original intention of the prefabricated building industry, namely to achieve higher cost effectiveness and production efficiency by rationally transforming the industry chain [11] (statistical data suggest that when the prefabrication rate reached 60%, the costs of prefabricated buildings (in comparison to traditional buildings) increased by 33.8%. This large gap in costs greatly undermined the subjective motivation to develop prefabricated buildings and seriously impacted industrial efficiency.).

Although China’s prefabricated construction industry has developed rapidly in recent years, this has been driven by policy, and the associated (high) costs have seriously hindered the development of the industry. Researchers have not engaged with this to a sufficient extent and so, in order to achieve the efficient and sustainable development of China’s prefabricated construction industry, it is necessary for this paper to analyze the current situation, clarify the stage of industrial development and explore factors that have limited the development of the industry. This will then provide a theoretical basis for the development of the country’s prefabricated construction industry.

2. Literature Review

2.1. Prefabricated Building Industry

2.1.1. Prefabricated Building Industry in China

Prefabricated buildings are based on the industrial production of prefabricated components as the core and a construction production technology that connects prefabricated components together on site after production. It is a modern construction technology that replaces traditional cast-in-place construction, which has attracted widespread attention in many countries in recent years [12].

A study in the U.K. showed that time, cost, quality, and productivity are the main factors that drive developers to adopt prefabricated construction [13]. Blismas et al. [14] investigated the building market of Australia from the perspective of sustainable development and found that the development of the prefabricated building industry is mainly driven by increased productivity and reduced labor demand. Zhai et al. [7] suggested that the advantages of prefabricated buildings, such as reduced construction waste and building energy consumption, have driven the development of the prefabricated building industry in China. Prefabricated construction is a mode of construction that is recommended on the basis of the fact that it saves energy, materials, water, and land resources and protects the environment. It reduces more than 40% of the carbon emissions throughout its full lifecycle, and this is one of the reasons why the “Notice by the State Council of the Action Plan for Carbon Dioxide Peaking Before 2030” proposes vigorously developing prefabricated buildings. In relying on the labor supply-side reform of the traditional building industry, the prefabricated building industry not only solved the problem of labor shortages, but also increased labor productivity by combining the industrial production of prefabricated members with mechanized installation and construction. The prefabricated building industry has developed rapidly in China because of its advantages including economic benefits, environmental benefits and social benefits. In seeking to create a favorable development environment for the prefabricated building industry, the Chinese government has issued many relevant policies. The “Opinions on Promoting the Green Development of Urban and Rural Construction” issued by the General Office of the State Council of the People’s Republic of China clearly pointed out that China should vigorously develop prefabricated buildings, focus on promoting the construction of steel structure prefabricated houses, continuously improve the standardization level of components, promote the formation of a complete industrial chain and promote the coordinated development of intelligent construction and construction industrialization. Additionally, the State Council of the People’s Republic of China issued the “Carbon Peak Travel Plan before 2030” clearly emphasized the need to promote green low-carbon building materials and green construction methods, accelerate the industrialization of new buildings, vigorously develop prefabricated buildings, promote steel structure housing and the recycling of building materials, along with the need to strengthen green design and green construction management. Moreover, the prefabricated building industry is a technology-oriented industry that has achieved technological innovation on the basis of the traditional construction industry in almost all aspects of the industry chain, including design, construction, and installation. In recent years, the “Guiding Opinions on Accelerating the Cultivation of Building Workforces in the New Era”, which were jointly issued by 12 departments, including the Ministry of Housing and Urban-Rural Development, have accelerated the improvement of a sound vocational training system, promoted the transformation and upgrades of the building industry, and provided talent support for the prefabricated building industry.

In the past few years, the production scale of prefabricated building enterprises in China has rapidly expanded [15]. Large-scale production has reduced product costs and boosted market competitiveness. However, an expanded production scale means higher costs of technological upgrading and, as a consequence, slower technological updating. As the country imposes increasingly higher requirements on prefabricated building technology systems, the capacity that does not satisfy market demands will be eliminated, which not only causes economic losses for enterprises but will also result in wasted resources and obstructed industrial development [16]. Moreover, as with the manufacturing industry, it has a stronger industry chain integration capability and requires considerable human (related technicians) and material (related equipment) early-stage inputs. When compared to the traditional construction industry, the prefabricated component production industry is a new industry in the prefabricated construction industry chain that must act cautiously in relation to cost control during transformation and upgrading, as this will help to avoid losses caused by excess capacity [17].

In addition, China has issued many policy incentives to promote the development of the prefabricated construction industry. For example, Beijing introduced the “Plan of Competitive Bidding for High-Standard Commercial Housing Construction” into the link of land auction. According to this plan, when the premium rate of land exceeds 15%, the ownership of land development will be determined by evaluating building construction schemes, livable technology applications, and management models provided by bidders. This plan enhances enterprises’ subjective motivation to develop prefabricated building technologies. The notable reduction in the premium rate of land from the previous 50% saves considerable capital for enterprises [18,19]. However, most existing policy subsidies are direct economic subsidies, such as the reduction in or exemption of construction fees and the exemption of charges for building waste disposal. However, enterprises seek to obtain policy subsidies by frequently choosing primary prefabricated members that only meet the lowest requirements for the prefabrication rate (such as prefabricated laminated plates). They adopt cast-in-place construction for the main structures, which is not conducive to the development of the country’s prefabricated building industry. In attending to this phenomenon, the country has gradually perfected relevant governmental subsidy strategies.

2.1.2. Research Gaps

Studies of China’s prefabricated industry show two limitations:

(1)

Due to the rapid development of the prefabricated construction industry in China, the scale of related enterprises has expanded rapidly. However, the rapid expansion of enterprise scale will waste resources and affect the industry’s sustainable development. In order to realize the sustainable development of China’s prefabricated construction industry, it is therefore necessary to study whether the existing scale of different enterprises in the prefabricated construction industry chain can meet the industry’s needs.

(2)

The Chinese government and relevant departments have issued many policies that provide prefabricated construction enterprises with clear incentives to promote the development of the prefabricated construction industry. However, researchers need to establish whether these incentives are effective.

2.2. Industry Efficiency

According to the traditional industrial organization theory that prevailed in the Western world at the early stage of industrial development, an industry is a collection of enterprises that produce similar products or provide similar services. With the development of economics, an increasing number of scholars, including Charnes and Cooper, introduced econometrics and statistics to the measurement of industrial efficiency, with the aim of analyzing the factors that influence industrial development and offering targeted suggestions on how to promote industrial development [20]. Farell [21] decomposed efficiency into overall technical efficiency and allocative efficiency, which, respectively, reflect the maximum output of an enterprise under a given input level and the ability of the enterprise to use the optimal input proportion under given technical and price levels. Depending on the specific fields involved, efficiency has been classified into overall technical efficiency and institutional efficiency, static and dynamic efficiency, as well as pure overall technical efficiency and scale efficiency. Overall technical efficiency reflects the comprehensive resource allocation and technical level of decision-making units. On the basis of an output perspective, overall technical efficiency can be defined as the possibility that an industry increases its output under a given production capacity and technical level [22,23]. Pure technical efficiency refers to the efficiency created by institutional and managerial levels. It represents the production efficiency of an enterprise affected by factors such as management and technology. A pure technical efficiency of 1 indicates that the use of input resources is efficient under the current technical level [24]. Scale efficiency refers to the difference between the existing scale and the optimal scale under given institutional and managerial levels. It is the production efficiency of an enterprise that is affected by scale and reflects the gap between the actual scale and the optimal scale. Factors such as imperfect competition and financial constraints may cause decision-making units to operate at a suboptimal scale. In this case, scale efficiency reflects the effect of optimal resource allocation on output units and serves as an important index for analyzing the optimal production scale [25,26]. Total factor productivity is the efficiency of production activities in a given period. It is a productivity index used to measure the total output per unit of total input, which is used to analyze the industry’s dynamic efficiency [27].

Given that enterprises (instead of industries) are behaving as subjects, overall technical efficiency is calculated from statistical data on enterprises. However, macro development trends and policy orientation also exert a huge influence on the overall technical efficiency of enterprises. Therefore, comprehensive analysis of industrial efficiency from the micro and macro levels can better identify the status of industrial development.

2.2.1. Micro-Level Industry Efficiency

A lot of research on micro-level industrial efficiency has mainly been focused on: enterprise scale; enterprise operation efficiency; and enterprise production efficiency [28,29,30,31].

Micro-level industrial efficiency is usually analyzed from the perspective of economic benefits. Due to the different attributes of industries, there are differences in the influencing factors of industrial efficiency at the micro level. When selecting input-output indicators at the micro-level, although the decision-making unit (DMU) can use any resource as an indicator, the correlation between the selected indicator and the industry affects the accuracy of industrial efficiency analysis [28]. The prefabricated building industry chain includes consulting industry (design), manufacturing industry (prefabricated component production), logistics industry (component transportation), and construction industry (construction). Therefore, it is necessary to comprehensively select the input and output indicators according to the attributes of each link of the prefabricated construction industry chain to improve the effectiveness of the efficiency analysis of the prefabricated construction industry at the micro level (

Table 1. Literature on micro-level industry efficiency.

No.	Authors	Research Topic	Methodologies	Input Indicator Selection	Output Indicator Selection
1	Page [28]	Enterprise scale	DEA-TFP	Firm size; age of the enterprise; levels of managerial education; training contribute	Vintage of the capital stock
2	Zhou et al. [29]	Enterprise operation efficiency	DEA—MRA	Fixed asset; salaries and wages; operating expenses; current liabilities	Operating income
3	Lee [30]	Enterprise operation efficiency	DEA-CCR&BBC	The number of total employees; Total expenditures; The number of branches; the number of partners	Attestation revenues; tax business revenues; management consultancy revenues; corporate registration
4	Jonsson [31]	Enterprise production efficiency	DEA-OLS	The number of total employees; the duration of manager presence	Operating income

).

2.2.2. Macro-Level Industry Efficiency

The research on industrial efficiency at the macro level mainly focuses on: regional industrial efficiency differences; new technology application level; input scale control capability; resource utilization; and environmental benefits [32,33,34,35,36].

In analyzing industrial efficiency at the macro level, based on the principle of reflecting the overall input and output of the industry as much as possible, indicators that have a great impact on industrial efficiency are selected [33]. In order to analyze the differences in regional industrial development, the industrial development level is usually divided into regions according to the different levels of regional development driven by the economic development plan. However, policies have dominated the development of China’s prefabricated construction industry, and the level of industrial development is usually evaluated by assembly rate and prefabrication rate, which leads to the fact that the efficiency of prefabricated construction industry in different regions is not necessarily directly related to the economic development of the region [32]. In addition, the prefabricated construction industry is the product of the transformation and upgrading of the traditional construction industry, which is a knowledge-intensive industry [5]. Compared with the traditional construction industry, the required employees are industrial workers with high technical level, which results in significantly fewer employees [4]. Therefore, the number of employees cannot accurately measure the impact of investment scale control ability on the efficiency of the prefabricated construction industry. When analyzing the efficiency of the prefabricated building industry, it is necessary to pay attention to the impact of policy, economy and technology on the industry [36] (

Table 2. Literature on macro-level industry efficiency.

No.	Authors	Research Topic	Methodologies	Input Indicator	Output Indicator
1	Xue et al. [32]	Regional industrial efficiency differences	DEA-Malmquist	The amount of total assets of the construction industry; the number of total employees in the construction industry	The amount of value added to the construction industry
2	Zhang and Liu [33]	New technology application level	DEA-PCA	The number of construction companies; technical equipment rate; total construction assets	Total construction assets; gross output value of construction industry
3	Ning and Li [34]	Input scale control capability	DEA	Total number of employees; construction area; total assets and total power of own equipment	Gross output value and completed area of construction industry
4	Li and Yin [35]	Resource utilization	DEA-SEM	Fixed assets of construction enterprises; number of employees in construction enterprises; total energy consumption of construction industry	Gross output value of construction industry
5	Kang et al. [36]	Industrial development efficiency	DEA-Tobit	Support policy; market scale; development of industry chain exhibition situation	Gross industrial output; industry employees

).

2.2.3. Research Gaps

There are three main limitations of the studies of China’s prefabricated industry.

(1)

Existing research focuses on the micro level or macro level when analyzing industrial efficiency, and rarely conducts a comprehensive analysis of industrial efficiency at the micro and macro levels. Enterprises (instead of industries) behave as subjects, and so macro development trends and policy orientations will have a huge influence on enterprises’ overall technical efficiency. When analyzing industrial efficiency, it is therefore necessary to comprehensively analyze micro-level and macro-level industrial efficiency, as this will improve the comprehensiveness of research and the accuracy of evaluation.

(2)

Scholars have extensively researched the efficiency of the construction industry, but the prefabricated construction industry is a new type of industry that transforms and enhances the traditional construction industry through technological innovation. At present, few researchers have addressed industrial efficiency. In order to identify the development level of China’s prefabricated construction industry, it will be instructive to refer to the efficiency analysis method of traditional construction industry. It can then be combined with an assessment of micro and macro level characteristics and used to analyze the efficiency of China’s prefabricated construction industry.

(3)

Current research on the efficiency of China’s prefabricated building industry focuses on three levels (policy, the economy and technology). However, the data sources of input and output indicators, such as the development of the prefabricated construction industry chain, are subjective. The use of qualitative indicators of this kind may result in inaccurate measures of industrial efficiency.

3. Methodology

3.1. Research Approach

This paper drew on previous research on industrial efficiency with the aim of selecting input and output indicators that relate to three dimensions (economy, environment and society). The micro and macro levels were then comprehensively analyzed on the basis of the sustainable development perspective.

3.2. Research Design

The efficiency of China’s prefabricated building industry was first measured at the micro level (including technical efficiency, pure technical efficiency and scale efficiency) and a dynamic analysis of the industrial efficiency was conducted at the micro level to explore the impact of technological change (TC) of prefabricated building-related enterprises on TFP during the sampling period. The same method was used to measure the efficiency of China’s prefabricated construction industry at the macro level. Finally, an empirical analysis of micro-level and macro-level factors that influence the efficiency of China’s prefabricated building industry was undertaken, and suggestions were put forward.

3.3. Area/Population of the Study

For both the micro and macro levels, we chose 25 Chinese provinces (cities) as research samples because the other provinces (cities) have a small population and large area, and their existing construction volume meets market demand. They have limited demand for the prefabricated construction industry, which means that they cannot be used as an evaluation area for the efficiency level of China’s prefabricated construction industry (this does not apply to the Hong Kong, Macao and Taiwan regions).

3.4. Sample Size

This study took 23 listed companies with different links in China’s prefabricated construction industry chain as micro-level research samples. The number of enterprises related to prefabricated buildings increased to 14,000 in the period 2011–2020 (excluding Hong Kong, Macao, and Taiwan), and 3301 related enterprises were newly registered in 2020 [37]. The 23 selected companies are representative of each stage of China’s prefabricated construction industry chain, and were selected because they effectively reflect the development status of China’s prefabricated construction industry at the micro level.

In accordance with the ranking of enterprises related to prefabricated buildings released in 2020, listed enterprises were selected as samples. In order to more clearly identify the efficiency of the prefabricated building industry in China at the micro level, the financial data of 23 enterprises listed on the Shanghai and Shenzhen Stock Exchanges were selected for analysis.

At the macro level, 25 provinces (cities) with high demand for the prefabricated construction industry were selected as research samples.

3.5. Types of Data

3.5.1. Micro-Level Data

When analyzing the efficiency of China’s prefabricated construction industry at the micro level, the input and output indicators were selected by referring to three aspects: enterprise scale, enterprise operation efficiency, and enterprise production efficiency. Prefabricated building-related companies included consulting companies (design companies), manufacturing companies (component production companies), and logistics companies, which are similar to the types of companies in the literature review; fixed assets, operational costs; and employee numbers were selected as input indicators and operating revenue was selected as the output indicator.

3.5.2. Macro-Level Data

We selected input and output indicators to analyze the macro-level industrial efficiency of China’s prefabricated buildings by referring to regional industrial efficiency differences, new technology adaptation levels, input scale control capabilities, resource utilization and industrial development efficiency perspectives.

Table A6. Selection of measurement indices for the efficiency of the prefabricated building industry.

		Index Name	Unit
Input index	X1	Year-end total number of construction machinery and equipment owned by building enterprises	Set
	X2	Year-end total power of construction machinery and equipment owned by building enterprises	10,000 kW
	X3	Technical equipment rate of building enterprises	Yuan/person
Output index	Y1	Labor productivity of building industry	Yuan/person
	Y2	Total output value of building industry	100 million yuan

shows the macro-level inputs and outputs of industrial efficiency.

3.5.3. Factors Influencing Industrial Efficiency Data

This study analyzed the factors that affect the efficiency of China’s prefabricated industry by drawing on a sustainable development perspective, and it selected indicators from the economic, environmental and social levels (see

Table A10. Factors influencing the efficiency of the prefabricated building industry at the micro level.

Variable Name	Model 1			Model 2
	Coefficient	Standard Deviation	Z Value	Coefficient	Standard Deviation	Z Value
TP	0.1889	0.0298077	6.34	0.1721	0.0285885	6.02
SUB	0	0.00000391	−3.37	0	0.0000037	−2.94
AT	−0.0373	0.0417651	−0.89	−0.0397	0.0402019	−0.99
SC	1.3331	0.8309285	1.6	1.1512	0.803723	1.43
lnSP	−0.0204	0.0058417	−3.50	−0.0177	0.0056052	−3.15
lnPEO	0.0332	0.0154231	2.15	0.0294	0.0146774	2
Subsidy square				0.928	0.0836784	11.09

,

Table A11. The test at significance levels of the prefabricated building industry at the micro level.

Variable Name	p Value
TP	0.000 ***
SUB	0.001 ***
AT	0.375
SC	0.112
lnSP	0.001 ***
lnPEO	0.034 **

Note: *** and ** indicate that the Z test values passed the test at significance levels of 0.01, 0.05, respectively.

and

Table A12. Factors influencing the efficiency of the prefabricated building industry at the macro level.

Variable	Coefficient	Standard Deviation	Z Statistic	p-Value
lnPOP	2.1059	0.5826276	3.61	0.001 ***
GOV	0.0004	0.0000818	4.49	0 ***
lnGDP	0.0883	0.1338114	0.66	0.512
lnNCA	0.0883	0.1338114	0.66	0.512
lnCDE	−0.0281	0.0144167	−1.95	0.055 *

Note: *** and * indicate that the Z test values passed the test at significance levels of 0.01, and 0.1, respectively.

).

3.6. Methods of Data Collection

The main micro-level data sources included the Wind database, the financial statements of listed enterprises, the National Bureau of Statistics database and public data (see

Table A1. Twenty-three prefabricated building listed enterprises in China.

No.	Enterprise	Product Type
1	Gold Mantis	Structures, civil buildings, decorative materials
2	CAPOL	Professional consulting services
3	ARTS Group	Industrial buildings, professional consulting services
4	Honglu Steel Structure	Steel building structures
5	Hangxiao Steel Structure	Steel building structures, civil buildings
6	Jinggong Steel	Steel building structures, civil buildings
7	Trendzone	Decoration engineering
8	YASHA	Decoration engineering
9	SCG	Civil buildings, residential buildings
10	CSCEC	Civil buildings, professional consulting services
11	CRPCC	Industrial buildings
12	TUS-DESIGN	Civil buildings, professional consulting services
13	Arcplus Group	Professional consulting services, decoration engineering
14	JANGHO	Steel building structures, decoration engineering
15	MHome Group	Architectural profiles
16	BNBM	Profile steel, bricks, tiles, and blocks
17	Southeast Space Frame	Steel plates, steel building structures
18	Fuhuang Steel Structure	Industrial buildings
19	CENTER INT	Prefabricated members
20	Vanke	Housing estate, prefabricated construction
21	Grandland Decoration	Decorative materials
22	Dongfang Risheng	Decoration engineering
23	Ningbo Construction	Industrial buildings, steel building structures, decoration engineering

).

The main macro-level data sources (see Table A6) were taken from the China Statistical Yearbooks on Construction, the statistical yearbooks of Chinese provinces (cities), and the website of the National Bureau of Statistics of China (https://data.stats.gov.cn, accessed on 5 April 2022). The use of these resources helped to ensure this paper’s validity and accessibility.

3.7. Method of Data Analysis

3.7.1. DEA Model

This paper focused on the problem of measuring the efficiency of China’s prefabricated building industry. Here, ‘industry efficiency’ means producing the maximum quantity of output possible with a given set of inputs. The production frontier is simply the maximum output possible for each combination of inputs. Decision-making units (DMU) that produce on the frontier are efficient, while DMU inside the frontier is inefficient. The efficiency of DMU can be evaluated by referring to TE (overall technical efficiency). When TE = 1, the DMU is efficient, and when the technical efficiency is <1, the DMU is inefficient. In order to analyze the inefficiency of DMU, PTE (pure technical efficiency) and SE (scale efficiency) were used to analyze TE. These measures of properties can be most easily interpreted by drawing on a measure of efficiency and data envelopment analysis (DEA). To evaluate “efficiency” and identify a range of values that would allow us to do this, we drew on Wu et al.’s (2018) tertiary study of the efficiency of power generation technologies in each Chinese province. This enabled us to distinguish high (TE ≥ 0.9), medium (0.6 ≤ TE < 0.9) and low (TE < 0.6) efficiencies [38].

We drew on the data on input and output indices, and used Deapv2.1 software and an input-oriented BCC model to calculate the efficiency of the prefabricated building industry in China.

3.7.2. Malmquist Index

After the efficiency analysis of China’s prefabricated construction industry is completed, the Malmquist index was used to dynamically analyze the efficiency of China’s prefabricated construction industry. This has a number of advantages: it does not require pricing information; it only needs to be calculated from quantitative data on inputs and outputs; it does not need to assume a way to maximize output or reduce inputs; it enables improved segmentation of changes in industrial efficiency, which provides different sources of variation; and finally, when inputs and outputs are added, it does not require standardized units of measure for the variables involved in the calculation. It is therefore well-suited to analyzing the dynamic efficiency of China’s prefabricated construction industry. The Malmquist index model is a dynamic efficiency analysis of the data of each DMU in different periods, including comprehensive technical efficiency changes and technological progress indexes. A dynamic analysis of the efficiency of the prefabricated building industry makes it possible to analyze how changes in the level of organization and management and technological progress impact the efficiency of the prefabricated building industry in the sampling period [39,40].

3.7.3. Tobit Model

The Tobit model is also known as the sample principle model and the restricted dependent variable model. When used to analyze the factors that influence the efficiency of the prefabricated building industry, it differentiates between various influencing factors and discretely distributes the data. It is therefore well-suited to analyzing the efficiency of the prefabricated building industry [41].

In order to construct a model, Stata software was used to perform Tobit regression analysis on (micro and macro) panel data taken from China’s prefabricated building industry in 2014–2020; and the Ols model was adopted to conduct a robustness test on evaluation indices. The results are provided in Table A10, Table A11 and Table A12. The empirical analysis was performed on the drivers of industrial development. The use of the model is shown in the following:

Micro level:

$$E{E}_{it}=C+{\alpha }_{1}T{P}_{it}+{\alpha }_{2}SU{B}_{it}+{\alpha }_{3}A{T}_{it}+{\alpha }_{4}S{C}_{it}+{\alpha }_5\ln S{P}_{it}+{\alpha }_6\ln PEO+{\epsilon }_{\mathrm{it}}$$

(1)

$$i=1,2,\cdots \cdots ,23;t=1,2,\cdots \cdots 7$$

(2)

where EE_it is the overall technical efficiency of 23 prefabricated building enterprises in 2014–2020, set as the overall technical efficiency result calculated by the DEA model in Section 2; C is a constant term; TP denotes the proportion of technicians; SUB denotes the amount of government subsidies; AT denotes asset turnover; SC denotes the ratio of expenses to sales; lnSP denotes the production scale; lnPEO denotes per capita output; ε is a stochastic disturbance term; i denotes enterprise; and t denotes the year. The results are shown in Table A10 and Table A11.

Macro level:

$$E{F}_{it}=\beta +{\alpha }_1\ln PO{P}_{it}+{\alpha }_{2}GO{V}_{it}+{\alpha }_3\ln GD{P}_{it}+{\alpha }_4\ln NC{A}_{it}+{\alpha }_5\ln CD{E}_{it}+{\epsilon }_{\mathrm{it}}$$

(3)

$$i=1,2,\cdots \cdots ,23;t=1,2,\cdots \cdots 7$$

(4)

where EF is the overall technical efficiency of the prefabricated building industry in 25 Chinese provinces (cities) in 2014–2020, which is set as the result calculated by the preceding DEA model; lnPOPit denotes population size; GOVit denotes the total output value of the building installation industry; lnGDPit denotes gross national product (GNP); lnNCAit denotes the area of new real estate projects; lnCDEit denotes carbon dioxide emissions; ε is a stochastic disturbance term; i denotes the province (city); and t denotes the year. Stata software was, in accordance with the model, used for an empirical analysis of influencing factors. The results are shown in Table A12.

3.8. Validity and Reliability of the Data

DEA is a novel interdisciplinary research method that determines the production frontier (i.e., the optimal production state) on the basis of the inputs and outputs of multiple decision-making units. It has been applied to management science, operations research, and econometrics [42,43]. DEA only imposes a few constraints on sample data and does not require a production function model to be built in advance. It can measure the efficiency of decision-making departments under data distribution uncertainty and is often used to evaluate industrial efficiency and corporate production efficiency [44,45,46,47,48,49,50]. DEA can evaluate the efficiency of multiple decision-making units with multiple inputs and outputs [51,52]. This paper drew on an input-oriented DEA model to analyze the efficiency of the prefabricated building industry in China.

With regard to the selection of indices, DEA has very few limitations, but when there are a limited number of decision-making units, this inevitably affects the validity of results. If an accurate evaluation of industrial efficiency is to be achieved, the first step is to select suitable input and output indices. This paper combined the indicators selected in the literature review to measure the industrial efficiency at the micro and macro levels, and selected the input and output indicators that meet the characteristics of the prefabricated building industry—this, it is believed, will help to ensure the validity and reliability of the data.

4. Results

4.1. The result of the Efficiency of the Prefabricated Building Industry in China at the Micro Level

4.1.1. Overall Technical Efficiency Analysis

According to

Table A2. Overall technical efficiency of prefabricated building enterprises in China (micro level, 2014–2020).

Enterprise	2014	2015	2016	2017	2018	2019	2020	Average
Gold Mantis	0.806	0.817	0.8	0.781	0.856	0.875	0.89	0.832
CAPOL	0.966	1.000	1.000	0.895	0.919	0.929	0.865	0.939
ARTS Group	0.935	0.831	0.886	0.815	0.834	0.843	0.891	0.862
Honglu Steel Structure	0.747	0.744	0.755	0.73	0.741	0.743	0.775	0.748
Hangxiao Steel Structure	0.762	0.796	0.874	0.916	0.798	0.775	0.832	0.822
Jinggong Steel	0.823	0.791	0.74	0.711	0.724	0.752	0.826	0.767
Trendzone	1.000	1.000	0.696	0.725	0.731	0.733	0.808	0.813
YASHA	0.831	0.809	0.781	0.728	0.723	0.744	0.801	0.774
SCG	0.764	0.77	0.759	0.729	0.801	0.871	0.838	0.79
CSCEC	0.786	0.825	0.905	0.779	0.821	0.843	0.872	0.833
CRPCC	0.992	0.88	0.727	0.787	0.884	0.833	0.874	0.854
TUS-DESIGN	1.000	1.000	1.000	1.000	0.923	0.917	0.909	0.964
Arcplus Group	0.899	0.986	0.999	0.86	0.839	0.824	0.888	0.899
JANGHO	0.726	0.683	0.723	0.799	0.824	0.855	0.943	0.793
MHome Group	1.000	0.987	1.000	1.000	0.832	0.914	0.883	0.945
BNBM	0.863	0.885	0.907	0.935	0.966	0.936	0.961	0.922
Southeast Space Frame	0.73	0.688	0.703	0.709	0.701	0.721	0.784	0.719
Fuhuang Steel Structure	0.718	0.732	0.709	0.713	0.737	0.765	0.75	0.732
CENTER INT	1.000	0.99	0.839	0.79	0.779	0.786	0.846	0.861
Vanke	1.000	1.000	1.000	1.000	1.000	1.000	1.000	1.000
Grandland Decoration	0.803	0.822	0.802	1.000	1.000	1.000	1.000	0.918
Dongfang Risheng	1.000	1.000	0.987	0.971	0.995	0.972	1.000	0.989
Ningbo Construction	0.753	0.767	0.755	0.695	0.684	0.693	0.756	0.729
Annual average	0.865	0.861	0.841	0.829	0.831	0.84	0.869	0.848

, seven enterprises had, on average, a technical efficiency above 0.9 (in 2014–2020), and the other 16 enterprises showed moderate overall technical efficiency (0.719–0.899). The average overall technical efficiency of 23 enterprises during the sample period was 0.848, which is much larger than 0.6, but slightly smaller than 0.9, meaning that, when considered at the micro level, the efficiency of China’s prefabricated building industry was slightly below the high level, but well above the medium level. In order to establish a clearer identification efficiency level, 0.75–0.9 was defined as slightly lower than the high level, and 0.6–0.74 was defined as slightly higher than the moderate level. The efficiency of China’s prefabricated construction industry at the micro level is therefore slightly below a high level.

Figure 1 shows that the efficiency of the prefabricated building industry in China has an average annual value of 0.829–0.869. This shows that, in the sampling period, the efficiency of China’s prefabricated construction industry showed considerable scope for improvement. In the period 2017–2020, the overall technical efficiency of the prefabricated building industry increased from 0.831 to 0.869. This could be explained in two ways. First, the government attached increasing importance to the development of the prefabricated building industry and constantly introduced favorable policies that would promote the industry’s development. Second, related enterprises gradually completed technological innovation and improved their capacity utilization rate and were therefore able to satisfy market demands while avoiding excess capacity. In 2016, new prefabricated buildings accounted for about 5% of the total area of new buildings in China. In the period 2017–2020, this proportion surged from 6.5% to 20.5%, and the market size expanded rapidly from 283.9 to 1227.7 billion yuan. However, when compared to the penetration of prefabricated buildings in developed countries (90% in the USA and Japan and 85% in France), it was clear there were still grounds for a substantial increase. China’s prefabricated building market is therefore still immature and in need of great improvement. When seen from a micro perspective, China’s prefabricated building industry had slightly below high-level efficiency.

4.1.2. Pure Technical Efficiency Analysis

Figure 1 shows that, on the basis of the overall trend in 2020, China’s prefabricated building industry had an average pure technical efficiency of 0.924 (>0.9) at the micro-level, which indicates that the utilization rate of resources reached a high level but still failed to reach the most effective state. It is worthwhile to note that China’s prefabricated building industry was in its infancy in 2015–2017, and the relative imperfections of relevant standards and policies led to an imperfect prefabricated building market. The rapid development of China’s real estate industry during this period caused the building industry to quickly expand. Building enterprises often tried to enhance their competitiveness through scale expansion while neglecting technical R&D, resulting in a decline in pure technical efficiency [53,54]. The “Action Plan for Prefabricated Construction in the 13th Five-Year Plan Period” proposed increasing the proportion of new prefabricated buildings in the total area of new buildings above 15% and increasing the prefabrication rate above 50% on a nationwide basis in 2018–2020. This action plan urged Chinese enterprises related to prefabricated buildings to employ technological upgrading as their core competitiveness measure, and this resulted in pure technical efficiency showing a rising trend in this period.

4.1.3. Scale Efficiency

Figure 1 shows that in 2020, these 23 Chinese prefabricated building enterprises had an average scale efficiency of 0.944 (>0.9), which means that the efficiency of the prefabricated construction industry at the micro level was at a high level in the country in this year. Scale efficiency presented an “S-shaped” trend over the entire sampling period. In 2020, three enterprises had a constant return to scale (RTS), and 15 had an increased RTS (IRS), implying that Chinese prefabricated building enterprises were mostly uneconomical. The data suggest that most Chinese enterprises related to prefabricated buildings had insufficient capacity and ineffective scale efficiency in this period. Before 2017, China’s prefabricated building industry had a small scale, and the old technical systems and talent pools were able to satisfy market demand. With the expansion of the industrial scale and the raising of industrial standards, the old technical systems could no longer satisfy market demands. In this situation, technological upgrading required a large amount of capital investment, a sufficient reserve of professional and technical talents and a long transformation cycle. The failure of enterprises to satisfy market demands with sufficient inputs caused a sharp drop in their post-2017 average scale efficiency. It should be noted that, of the five enterprises with declining scale efficiency in 2020, four were prefabricated member manufacturers. This was because the requirement for the prefabrication rate was low in the sampling period; in seeking to reduce costs associated with the downstream enterprises’ input, the industry chain would choose low-cost, single-category prefabricated members that met the requirements for the prefabrication rate (such as prefabricating laminated plates) [55]. As a result, most production lines of prefabricated member manufacturers were idle, which resulted in input-oriented RTS declining. As China raised its requirement for the prefabrication rate, more categories of prefabricated members had to be applied to meet this requirement. This clearly alleviated the problem of the low-capacity utilization rate that confronted prefabricated member manufacturers [56]. This is an important study because it strengthens the conclusion that enterprises can improve the efficiency of the prefabricated building industry by expanding production scale to meet market demand.

4.2. The Malmquist Index Result

4.2.1. Overall Technical Efficiency Change

Table A3. Pure technical efficiency of prefabricated building enterprises in China (micro level, 2014–2020).

Enterprise	2014	2015	2016	2017	2018	2019	2020	Average
Gold Mantis	0.851	0.846	0.811	0.786	0.873	0.881	0.897	0.849
CAPOL	1.000	1.000	1.000	0.901	1.000	1.000	0.978	0.983
ARTS Group	1.000	0.862	0.893	0.816	0.874	0.893	1.000	0.905
Honglu Steel Structure	0.803	0.78	0.771	0.75	0.744	0.749	0.793	0.77
Hangxiao Steel Structure	0.794	0.848	0.89	0.946	0.803	0.787	0.836	0.843
Jinggong Steel	0.824	0.811	0.755	0.723	0.736	0.786	0.844	0.783
Trendzone	1.000	1.000	0.701	0.732	0.948	1.000	1.000	0.912
YASHA	0.85	0.82	0.782	0.745	0.753	0.751	0.802	0.786
SCG	0.765	0.776	0.785	0.735	0.805	0.876	0.841	0.798
CSCEC	1.000	1.000	1.000	1.000	1.000	1.000	1.000	1.000
CRPCC	1.000	1.000	1.000	1.000	1.000	1.000	1.000	1.000
TUS-DESIGN	1.000	1.000	1.000	1.000	1.000	1.000	1.000	1.000
Arcplus Group	1.000	1.000	1.000	1.000	1.000	1.000	1.000	1.000
JANGHO	0.727	0.683	0.761	0.813	0.846	0.875	0.966	0.81
MHome Group	1.000	1.000	1.000	1.000	1.000	1.000	1.000	1.000
BNBM	0.974	0.986	1.000	1.000	0.968	0.942	1.000	0.981
Southeast Space Frame	0.762	0.751	0.72	0.724	0.706	0.732	0.79	0.741
Fuhuang Steel Structure	0.72	0.732	0.719	0.724	0.744	0.783	0.756	0.74
CENTER INT	1.000	1.000	0.84	0.827	1.000	1.000	1.000	0.952
Vanke	1.000	1.000	1.000	1.000	1.000	1.000	1.000	1.000
Grandland Decoration	0.826	0.824	0.804	1.000	1.000	1.000	1.000	0.922
Dongfang Risheng	1.000	1.000	1.000	0.997	1.000	0.995	1.000	0.999
Ningbo Construction	0.758	0.767	0.756	0.701	0.709	0.71	0.757	0.737
Annual average	0.898	0.891	0.869	0.866	0.892	0.903	0.924

shows that, in the period 2014–2020, the overall technical efficiency change index of China’s micro-level prefabricated building industry was 1.002, indicating a rising trend. This was related to an increased number of technicians and the expanded market size of China’s prefabricated building industry [17]. Overall, technical efficiency was mainly affected by scale efficiency in the sampling period. In particular, in 2017–2018, the pure technical efficiency change was 1.031 and presented a rising trend, while the scale efficiency change was 0.974. This indicated that the production capacity of enterprises could satisfy market demands, but their production scale failed to keep pace with the expansion of the industrial scale, resulting in the slow rise in overall technical efficiency.

4.2.2. Technological Progress Change

In 2014–2020, the average technological progress change of China’s prefabricated building industry in micro-level was 1, and it followed a declining trend from 2015–2016 onwards, when it fell to 0.907. The rate of technological progress change was lower than that of pure technical efficiency change in the same period. Technological progress change was less than 1 in 2017–2020, implying that technological progress was generally ineffective. The design, production, installation, and other key technologies of prefabricated members determine industrial efficiency, and technological progress is mainly reflected in the production and installation of prefabricated members. In China, technical systems related to the production and installation of prefabricated members are largely imported from abroad. Although certain technical systems are self-developed, their technological progress efficiency is low [57].

4.2.3. Total Factor Productivity Change

Table A5. Total factor productivity of prefabricated building enterprises in China (2014–2020).

Year	Overall Technical Efficiency Change (EC)	Technological Change (TC)	Pure Technical Efficiency Change (Pech)	Scale Efficiency Change (Sech)	Total Factor Productivity Change (TFP)
2014–2015	0.995	1.028	0.992	1.003	1.046
2015–2016	0.976	0.959	0.975	1.002	0.907
2016–2017	0.986	1.059	0.996	0.990	1.032
2017–2018	1.004	0.997	1.031	0.974	1.012
2018–2019	1.012	0.987	1.013	0.999	0.998
2019–2020	1.037	0.968	1.026	1.010	1.008
Average annual rate of change	1.002	1.000	1.006	0.996	1.001

shows that in 2014–2020, the average total factor productivity of China’s prefabricated building industry in micro-level was 1.001 and followed a declining trend. In 2016–2017, total factor productivity clearly increased, with a change index of 1.032. This was due to a series of favorable policies introduced by the government with the aim of promoting the development of the prefabricated building industry. It is noteworthy that the period 2015–2016 witnessed the lowest total factor productivity in the sampling period, and both overall technical efficiency and technological progress were ineffective. Under the influence of the rapid development of China’s prefabricated building industry in 2016–2017, total factor productivity rose from a low point (in the sampling period) of 0.907 to a peak value of 1.032. This further demonstrated the need to comprehensively develop China’s prefabricated building industry.

4.3. The Result of the Efficiency of the Prefabricated Building Industry in China at the Macro Level

4.3.1. Overall Technical Efficiency

Figure 2 shows that the overall technical efficiency of China’s prefabricated building industry was between 0.655 and 0.763 at the macro level (in the sampling period) and peaked in 2018–2019.

Table A7. Overall technical efficiency of China’s prefabricated building industry (macro level, 2014–2020).

Area	2014	2015	2016	2017	2018	2019	2020	Average
Beijing	1.000	1.000	1.000	1.000	1.000	1.000	0.871	0.982
Tianjin	0.854	0.689	0.893	0.748	0.499	0.7	0.527	0.701
Hebei	0.498	0.487	0.517	0.424	0.154	0.523	0.524	0.447
Shanxi	0.372	0.337	0.352	0.409	0.37	0.488	0.419	0.392
Liaoning	0.613	0.559	0.557	0.548	0.501	0.349	0.43	0.508
Jilin	0.896	0.734	0.801	0.94	0.623	0.997	1.000	0.856
Heilongjiang	0.547	0.373	0.445	0.521	0.391	0.627	0.44	0.478
Shanghai	0.891	1.000	1.000	1.000	1.000	1.000	1.000	0.984
Jiangsu	0.724	0.76	0.813	0.819	1.000	1.000	0.827	0.849
Zhejiang	1.000	1.000	1.000	1.000	1.000	1.000	1.000	1.000
Anhui	0.528	0.809	0.702	0.68	0.709	0.729	0.965	0.732
Fujian	0.898	0.848	0.851	0.977	1.000	0.991	1.000	0.938
Jiangxi	0.743	0.695	0.734	0.58	0.633	0.659	0.766	0.687
Shandong	0.465	0.512	0.472	0.462	0.585	0.456	0.374	0.475
Henan	0.491	0.417	0.461	0.477	0.49	0.572	0.5	0.487
Hubei	0.505	0.783	0.812	0.911	0.637	0.737	0.736	0.732
Hunan	0.393	0.47	0.439	0.509	0.504	0.685	0.687	0.527
Guangdong	0.531	0.578	0.508	0.644	0.6	0.693	0.79	0.621
Chongqing	1.000	0.987	1.000	1.000	1.000	1.000	1.000	0.998
Sichuan	0.635	0.811	0.91	0.71	0.838	0.985	1.000	0.841
Guizhou	1.000	0.932	0.856	0.912	0.817	1.000	1.000	0.931
Yunnan	0.406	0.456	0.557	0.713	0.266	0.743	0.621	0.537
Shaanxi	0.394	0.469	0.586	0.712	0.638	0.648	0.608	0.579
Gansu	0.616	0.469	0.561	0.53	0.495	0.661	0.327	0.523
Xinjiang	0.544	0.744	0.714	0.729	0.631	0.833	0.97	0.738
Annual average	0.662	0.677	0.702	0.718	0.655	0.763	0.735	0.702

shows that, at the provincial level, provinces (cities) with an overall technical efficiency above 0.9 in 2014–2020 included Beijing, Shanghai, Zhejiang, Fujian, Chongqing, and Guizhou, which means that these provinces (cities) had a high level of prefabricated construction efficiency. However, during the sampling period, the overall technical efficiency was 0.702. When this paper’s threshold range is considered at the macro level, it becomes clear that the overall efficiency of the prefabricated building industry in China is slightly in excess of the moderate level.

However, the continuous expansion of China’s prefabricated building industry notwithstanding, its overall technical efficiency remains stagnant. In seeking to explain this phenomenon, this paper proposes dividing technical efficiency into pure technical efficiency and scale efficiency.

4.3.2. Pure Technical Efficiency

As seen in Figure 2, the pure technical efficiency of the prefabricated building industry in China mostly exceeded 0.75, and remained smaller than 0.9 in the sampling period, suggesting the country’s prefabricated building industry slightly exceeded moderate level efficiency. The provinces (cities) with an extremely pure technical efficiency (≥1) are Beijing, Jiangsu, Zhejiang, Hubei, Chongqing, and Guizhou. In contrast, provinces (cities) such as Shandong, Shanxi, and Liaoning had a pure technical efficiency of less than 0.6. This attests to the strong regional variations in the development of China’s prefabricated building industry.

4.3.3. Scale Efficiency

The results in Figure 2 show that the overall scale efficiency of China’s prefabricated building industry declined in the period 2014–2020. The average scale efficiency in 2020 was 0.892, and only 13 provinces (cities) reached the optimal scale, suggesting that the country should further increase its resource input in the prefabricated building industry.

The results in

Table A9. Scale efficiency of China’s prefabricated building industry (macro level, 2014–2020).

Area	2014	2015	2016	2017	2018	2019	2020	Average
Beijing	1.000	1	1	1	1	1	0.871	0.982	-
Tianjin	0.993	0.999	0.979	0.974	0.677	0.881	0.709	0.887	irs
Hebei	0.658	0.979	0.885	0.951	0.809	0.73	0.891	0.843	-
Shanxi	0.886	0.745	0.735	0.839	0.808	0.996	0.985	0.856	irs
Liaoning	0.981	0.94	0.867	0.956	0.792	0.758	0.963	0.894	irs
Jilin	0.896	0.734	0.801	0.94	0.678	0.997	1	0.864	-
Heilongjiang	0.784	0.57	0.619	0.589	0.452	0.702	0.58	0.614	irs
Shanghai	0.980	1	1	1	1	1	1	0.997	-
Jiangsu	0.724	0.76	0.813	0.819	1	1	0.827	0.849	drs
Zhejiang	1.000	1	1	1	1	1	1	1.000	-
Anhui	0.955	0.961	0.925	0.903	0.825	0.822	0.965	0.908	-
Fujian	0.997	0.971	0.989	0.977	1	0.992	1	0.989	-
Jiangxi	0.964	0.831	0.901	0.847	0.982	0.921	0.914	0.909	irs
Shandong	0.998	0.921	0.969	0.99	0.913	0.853	0.752	0.914	drs
Henan	0.984	0.978	0.999	0.954	0.895	0.913	0.896	0.946	irs
Hubei	0.505	0.783	0.812	0.911	0.637	0.737	0.736	0.732	-
Hunan	0.938	0.903	1	0.987	0.996	0.961	0.962	0.964	irs
Guangdong	0.842	0.998	0.832	0.814	0.768	0.737	0.79	0.826	-
Chongqing	1.000	0.987	1	1	1	1	1	0.998	-
Sichuan	0.984	0.995	0.971	0.989	0.949	0.985	1	0.982	-
Guizhou	1.000	0.932	0.856	0.912	0.817	1	1	0.931	-
Yunnan	0.858	0.754	0.768	0.889	0.772	0.891	0.912	0.835	irs
Shaanxi	0.987	0.949	0.981	0.965	0.917	0.836	0.885	0.931	irs
Gansu	0.883	0.788	0.69	0.686	0.624	0.837	0.68	0.741	irs
Xinjiang	0.828	0.776	0.714	0.729	0.631	0.841	0.97	0.784	-
Annual average	0.905	0.890	0.884	0.905	0.838	0.896	0.892

Note: irs indicates increasing returns to scale and drs indicates diminishing returns to scale.

illustrate the problem of irrational resource allocation in China’s prefabricated building industry, which could potentially increase its capacity. In 2020, the cumulative area of new prefabricated buildings in China reached 630 million m², with a year-on-year growth of 50%, accounting for about 20.5% of the total area of new buildings. This development trend was especially apparent in East China. In 2020, the proportion of new prefabricated buildings in the total area of new buildings reached up to 91.7% in Shanghai and exceeded 30% in both Jiangsu and Zhejiang. Although China’s prefabricated building industry is already well-established, 12 provinces (cities) still failed to reach an optimal scale in 2020. Of them, 10 provinces (cities) showed an IRS, which suggests that the government should respond to the demands generated by an expanding industrial scale by increasing investment in resource allocation.

However, the lack of subjective motivation in the development of the country’s prefabricated building industry can also be attributed to irrational resource allocation. If we assume a 7% annual increase in labor costs in China, in 2024, the cost of prefabricated buildings with a 50% prefabrication rate will be equal to that of cast-in-place buildings. If investment in the prefabricated building industry increases, the cost of prefabricated buildings will be lower than that of cast-in-place buildings, and this will effectively enhance the subjective motivation to develop prefabricated buildings.

4.3.4. Efficiency Comparison

The analysis in Figure 2 shows that the average efficiency of China’s prefabricated building industry was less than 1 in the sampling period, indicating that it had not reached an effective state. The overall technical efficiency and pure technical efficiency of the prefabricated building industry are generally increasing, suggesting that the technical level of China’s prefabricated building industry is gradually improving. Scale efficiency has always been higher than pure technical efficiency, implying that the improvement in the efficiency of China’s prefabricated building industry mainly depends on the scale of the increase in the prefabricated construction industry. This is consistent with the development status of China’s prefabricated building industry. In 2014–2016, the scale efficiency of prefabricated buildings in China decreased, while the pure technical efficiency showed an increasing trend and overall technical efficiency continued to increase. In summary, technological improvement also played an important role in promoting industrial development.

5. Discussion

This analysis of the efficiency of China’s prefabricated building industry (at both micro and macro levels) showed that the industry’s overall technical efficiency still has considerable room for improvement. Under the influence of pure technical efficiency and scale efficiency, the overall technical efficiency of prefabricated buildings is low, and pure technical efficiency is clearly lower than overall technical efficiency, which further illustrates the need for technological improvement. In considering the characteristics of the prefabricated building industry, we summarized the factors that influence the efficiency of the prefabricated building industry, such as technology, resources, policies, market and capital. The Tobit model was used to perform regression analysis on these factors.

5.1. Factors Influencing the Efficiency of the Prefabricated Building Industry at the Micro Level

• The p value of the proportion of technicians is 0 < 0.01, which means it passed the significance test at an extremely significant level—the Z value was 6.34, which is positive and higher than other factors. On this basis, the null hypothesis (“the proportion of technicians could not pass the significance test”) was rejected and the alternative hypothesis (“the proportion of technicians could pass the significance test”) was accepted. That indicates that technicians play a key role in improving the efficiency of the prefabricated building industry—by implication, when the proportion of technicians is higher, the enterprise is more competitive.
• The proportion of government subsidies passed the significance test (p = 0.001 < 0.01), and the Z value was −3.37. On this basis, the null hypothesis (“the proportion of government subsidies could not pass the significance test”) was rejected and the alternative hypothesis (“The proportion of government subsidies could pass the significance test”) was accepted. Government subsidies were negatively correlated with overall technical efficiency, which suggested that current policy subsidy strategies for the prefabricated building industry are ineffective. Policy subsidies can be regarded as a form of economic subsidy. Policy subsidies of this kind not only create economic benefits for enterprises, but also effectively promote the development and increased efficiency of the prefabricated building industry.
• The p values of asset turnover and the ratio of expenses to sales are, respectively, 0.375 and 0.112. Both exceed 0.01, and therefore fail to pass the two models’ significance tests. This suggests that neither one clearly influences overall technical efficiency. Asset turnover and the ratio of expenses to sales are both important indices that measure enterprises’ management efficiency. On the basis of these conclusions, the management level of related enterprises currently has no substantial influence on overall technical efficiency in China. Industrial development should be promoted by stressing technological upgrading and increasing production efficiency. Simply expanding the production scale without considering the actual market demand will waste resources and negatively impact the development of the prefabricated construction industry. It is therefore necessary to improve the resource allocation capacity, reasonably control the production scale and improve the prefabricated construction industry’s efficiency. This is supported by Mitchell (2002), who asserted the level of innovation will affect the reduction production scale [58].
• The production scale passed the significance test at 0.001, which demonstrated a negative correlation with overall technical efficiency. On this basis, the null hypothesis (“the production scale could not pass the significance test”) was rejected and the alternative hypothesis (“the production scale could pass the significance test”) was accepted. This means that although the production scale will have an impact on the efficiency of the prefabricated building industry, expanding the production scale of prefabricated buildings will not improve the industrial efficiency.
• In the Tobit and Ols model, the per capita output value was positively correlated with the overall technical efficiency of the prefabricated building industry; in addition, it passed the significance test, and also considerably influenced the overall technical efficiency of the prefabricated building industry (the Z value is 2.15 and the p value is 0.034 < 0.05). The per capita output value is an important index that measures an organization’s ability to create benefits [59]. It reflects the extent to which the prefabricated building industry is developed and the level of building industrialization. In being driven by the development of the prefabricated building industry, traditional builders will gradually transform into industrial workers. This change will alleviate the labor shortage that confronts the Chinese building industry, improve the production capacity of those involved in the building industry and cultivate a benign industry chain cycle.

5.2. Factors That Affect the Efficiency of the Prefabricated Building Industry at the Macro Level

• The Z value of population size was 3.61 and the p value was 0.001, which demonstrated that population size had a positive and significant influence on the development of the prefabricated building industry. On this basis, the null hypothesis (“the population size could not pass the significance test”) was rejected and the alternative hypothesis (“the population size could pass the significance test”) was accepted. The prefabricated building industry is based on the building industry’s transformation and upgrading. For this industry, population size represents market demand. In addition, the population size of a city is affected by its level of development. A more developed city therefore usually has a higher population inflow and, as a consequence, a larger number of technical talents. The reserve of technical talents also promotes the development of the prefabricated building industry.
• Table A12 shows the Z value of the total output value of the building installation industry was 4.49. This positive correlation, along with a p value of 0, indicated it had the most significant influence on prefabricated buildings. On this basis, the null hypothesis (“the total output value of the building installation industry could not pass the significance test”) was rejected and the alternative hypothesis (“the total output value of the building installation industry could pass the significance test”) was accepted. Prefabricated buildings are assembled onsite with prefabricated members. The development scale of the building installation industry therefore directly reflects the prefabricated building industry’s development status. The introduction of “Made in China 2025” has vigorously promoted China’s industrialization and modernization process, further elevated the (technical, process and equipment) levels of the building installation industry and contributed to the efficient development of the country’s prefabricated building industry.
• The p values of both GDP and the area of new real estate projects exceeded 0.01. Although they positively influenced the prefabricated building industry’s overall technical efficiency, neither passed the significance level. Neither GDP nor the area of new real estate projects positively influences the prefabricated building industry’s development. This conclusion also confirms that market size is not the core driver of the prefabricated building industry’s development.
• Carbon dioxide emissions have a significant negative influence on the prefabricated building industry’s development (Z value is −1.95 and p value is 0.055 < 0.1). This suggests that reducing carbon dioxide emissions promotes the prefabricated building industry’s development.

6. Conclusions

This paper uses the DEA method to calculate the overall technical efficiency of China’s prefabricated building industry at the micro and macro levels and analyzes the dynamic efficiency of China’s prefabricated building industry by applying the Malmquist index model. It also conducts an empirical analysis that addresses the factors that influence the efficiency of China’s prefabricated building industry.

In observing that the micro-level industrial efficiency of prefabricated buildings in China is slightly lower than a high level, we conclude that industrial transformation should be accelerated, and that the industrial penetration rate should be strengthened. The average pure technical efficiency of the micro-level prefabricated building industry during the sampling period was 0.924 (greater than 0.9), which means the pure technical efficiency attained a high level, but fell short of the most effective state. It was found that the behavior of prefabricated construction enterprises who gradually took technological innovation as the core competitiveness of enterprises as a result of policy change also contributed to a micro-level rise in pure technical efficiency during the sampling period. This also shows that, when compared to the traditional construction industry, the prefabricated construction industry is no longer a resource-intensive industry, and technological innovation has promoted the industry’s development. Scale efficiency at the micro level is also at a high level (average 0.944 > 0.9). It is worthwhile to note that, of the five companies with diminishing returns to scale in 2020, four were engaged in the production of prefabricated components. This shows that the investment scale of China’s prefabricated component production industry has not been reasonably controlled: resources have been wasted, which has weakened the sustainable development of China’s prefabricated building industry. The dynamic analysis of the efficiency of China’s prefabricated construction industry at the micro level shows that total factor productivity increased during the sampling period, and this further underlines the need to develop the country’s prefabricated construction industry.

China’s macro-level prefabricated construction industrial efficiency is slightly above the medium level. There are large differences in pure technical efficiency in different regions, which attests to regional differences in the development of China’s prefabricated construction industry. In addition, in 2020, of the 12 provinces (cities) that did not achieve optimal returns to scale, 10 provinces (cities) showed increasing returns to scale. This also shows that China should expand its investment in the prefabricated construction industry to meet market demand.

Analysis of the factors that affect the efficiency of China’s prefabricated construction industry show that the proportion of technicians has the greatest impact on industrial efficiency at the micro level—there is a positive correlation, and this shows that prefabricated buildings are knowledge-intensive industries. In addition, policy subsidies and production scale are negatively correlated with micro-level industrial efficiency. This shows that prefabricated construction enterprises should reasonably control the production scale and that existing policy subsidy methods have not promoted the efficiency of prefabricated buildings. It is worthwhile to note that carbon dioxide emissions are negatively correlated with the efficiency of the prefabricated building industry at the macro level. This shows that the development of the prefabricated building industry produces clear benefits and reduces carbon dioxide emissions. From an environmental perspective, this proves that the prefabricated building industry is part of the sustainable development industry. This paper concludes by offering specific recommendations to enterprises and governments that will help them to improve the efficiency of their national prefabricated construction industry.

At the corporate level, technological upgrading should become the core driver of the development of the prefabricated building industry. The prefabricated building industry is still an emerging industry in China. As it further develops, greater stresses will be imposed on product quality and capacity utilization demand, meaning that the development model of the traditional building industry will no longer be suitable for the prefabricated building industry. In this case, enterprises should focus on optimizing resource allocation and make further efforts to promote industrial development by extending the width and depth of the industry chain, and they should do this by improving and strengthening it. Investment in technological innovation should also be increased, and more technical systems should be developed that will meet the needs of different regional prefabricated building markets. For example, enterprises should not just meet the seismic demands, but should also resolve the various difficulties involved in connecting vertical prefabricated members in prefabrication construction—this will accelerate the transition of prefabricated buildings from the growth stage to the maturation stage.

The government should raise the access threshold of the prefabricated building industry, optimize resource allocation, and eliminate ineffective capacity that no longer satisfies market demands. Second, old policy subsidy strategies should acknowledge that carbon dioxide emissions have a negative impact on the prefabricated building industry, and this should be reflected by adjustments to R&D subsidies and reductions in/exemptions from taxes and fees. The sustainable development of prefabricated buildings should instead be emphasized, and follow-up policies in this field should be formulated, with specific emphasis on the social and environmental benefits of prefabricated buildings, such as those outlined in the “Plan of Competitive Bidding for High-Standard Commercial Housing Construction”. In acknowledging this, the government should strengthen the environmental regulations that apply to carbon emissions, as this will drive the rapid development of the prefabricated building industry. Finally, the country’s prefabricated building industry should not only optimize its resource allocation ability, but should also maintain the momentum of technological improvement with the aim of ensuring efficient industrial development. The prefabricated building industry should also accelerate the training of technicians in order to promote the industry’s sustainable development. Enterprises should, in referring to the technical requirements of the prefabricated building industry, flexibly adjust their production scale and guarantee the sustainability of technological innovation, as this will help to ensure the effectiveness of capacity.

This paper applies existing data to empirically analyze the efficiency of the prefabricated building industry in China; however, it only drew on a limited number of samples and equally limited data. As the industry matures, future researchers should expand the sample scale and continuously track the dynamic efficiency of the country’s prefabricated building industry, as this will help to improve research effectiveness.