Analysis of the Current Situation of the Construction Industry in Saudi Arabia and the Factors Affecting It: An Empirical Study

Abstract

The construction industry in Saudi Arabia has been modernized through the implementation of green building technologies and intelligent building systems, which have facilitated the sustainable development of the construction industry in Saudi Arabia. However, there is a paucity of research on the current situation of the construction industry in Saudi Arabia. In this study, the super-efficient epsilon-based measurement (EBM)–Malmquist model was used to measure the static and dynamic efficiency of the construction industry in the administrative areas of the 13 provinces of Saudi Arabia from 2013 to 2022, and the Tobit model was used to empirically analyze the factors affecting the efficiency of the industry. In addition, the spatial autocorrelation of the efficiency of the industry was analyzed using Moran’s Index (MI) to study the characteristics of the spatial distribution of industrial efficiency as well as the effectiveness of resource allocation. The study shows that Saudi Arabia’s construction industry is at a low level of development; the population, GDP, and carbon dioxide emissions have a significant impact on the efficiency of the industry; and the development of the industry can help to reduce carbon dioxide emissions, thus promoting environmental sustainability; Saudi Arabia’s construction industry has a spatial spillover effect but does not have a significant agglomeration effect. This study accurately identifies the current situation of the development of the construction industry in Saudi Arabia and proposes several countermeasures and opinions, which are expected to provide a theoretical basis for realizing its sustainable development.

1. Introduction

As the largest country in the Middle East, Saudi Arabia’s development has been in the spotlight of the international community. In order to reduce its dependence on oil and promote economic diversification and sustainable development, Saudi Arabia has put forward the “Vision 2030” plan [1]. One of the core objectives of the plan is to modernize and transform the country’s economy through large-scale infrastructure and urban development projects [2]. First, Vision 2030 emphasizes the development of a non-oil economy, which includes a significant investment in industry, services, and tourism to drive the demand for commercial and industrial buildings of all types. Second, Vision 2030 includes a number of large-scale construction projects, such as the NEOM Smart City and Red Sea projects, which will require a large number of new buildings to meet their functional needs and infrastructure [3]. In addition, in order to adapt to the significant population growth and accelerated urbanization, the Saudi Arabian government is promoting housing, urban development, and construction through the provision of loan support and land subsidies and the encouragement of private investors to participate in the construction of housing projects, etc. These policies not only enhance the market demand for the construction industry and alleviate the housing shortage problem but also promote the development of the related industrial chain and the improvement in the function of the city [4].

In recent years, the Saudi Arabian construction industry has undergone significant changes in response to rising market demand, a transformation driven by multiple factors, including technological advances and policy orientation. With the increase in high-end residential and commercial complexes and large-scale infrastructure projects and in order to meet the growing market demand for high-quality high-performance buildings, the Saudi Arabian construction industry has been transformed and upgraded to a more resource-intensive industry with higher capital and technological investments through technological innovations, policy support, and market-driven efforts. In addition, in order to reduce the dependence on low-skilled labor due to the rapid expansion of the construction industry, advanced technologies such as Building Information Modeling (BIM), 3D printing, drones, and robots have been introduced, which are not only widely used in the construction phase but have also been extended to design concepts and project management; these have significantly improved the efficiency and quality of construction while reducing the dependence on a large number of low-skilled laborers, realizing the transformation of the construction industry from resource intensive to technology intensive [5]. In addition, the Saudi government announced in 2021 that it plans to achieve carbon neutrality by 2060, which undoubtedly poses a strong challenge, as it has one of the highest per capita carbon emissions in the world. In order to reach this goal as soon as possible, the Saudi Arabian government has developed stricter environmental regulations and policies to promote the transition of the construction industry toward green building and sustainability [6]. These initiatives include environmental impact assessment requirements for construction projects, the introduction of green building standards and certification systems, the promotion of carbon capture storage (CCS) technologies, and increased requirements for the use of energy-efficient materials and technologies in new and retrofit construction projects, thereby realizing the reduction in building energy consumption and carbon emissions [7,8]. Meanwhile, the introduction of policy incentives such as tax breaks and financial subsidies has further encouraged construction companies to invest in green technologies and green building projects. These not only enhance the environmental awareness and social responsibility of Saudi Arabia’s construction industry but also accelerate the technological innovation and industrial upgrades, leading Saudi Arabia’s construction industry toward sustainable development. However, the international economic environment and geopolitical relations have affected the efficient development of Saudi Arabia’s construction industry. The COVID-19 epidemic led to a decrease in other countries’ direct investment in Saudi Arabia affecting the source of funding for construction projects in Saudi Arabia [9]. In addition, the uncertainty of national revenues due to oil price fluctuations and the instability of regional relations have also directly or indirectly negatively affected the Saudi Arabian construction industry.

In summary, the Vision 2030 program has promoted the efficient development of Saudi Arabia’s construction industry; however, the changes in the global political and economic situation have also brought new opportunities and challenges for Saudi Arabia’s construction industry. Scholars have conducted in-depth research on Saudi Arabia’s construction industry; however, the quantitative research on its efficiency is still insufficient, making it difficult to accurately identify its current situation. The objective of this study was to elucidate the current status of development within the construction industry in Saudi Arabia. By employing a quantitative methodology to assess the efficiency of the industry, it is possible to gain a more accurate representation of the actual development situation, thereby ensuring the scientific rigor and credibility of the research conclusions. Furthermore, this approach helps to avoid the potential impact of subjective bias on the results of a qualitative analysis. Moreover, this analytical approach can assist in the identification of the key strengths and weaknesses of the construction industry in Saudi Arabia, thereby facilitating the formulation of targeted improvement strategies and optimization pathways for policymakers and industry practitioners.

The research approach is as follows: First, the super-efficient EBM model was employed to calculate the static efficiency of the Saudi Arabian construction industry, encompassing technical efficiency, pure technical efficiency, and scale efficiency, based on the pertinent data of the construction industry in Saudi Arabia over the past decade. Subsequently, the Malmquist index model was employed to calculate the dynamic efficiency, thereby enabling an analysis of the development status and future development trend of the Saudi Arabian construction industry. Subsequently, a Tobit model was constructed to analyze the empirical factors affecting the efficiency of the Saudi Arabian construction industry and identify the primary influencing factors. Furthermore, the spatial autocorrelation of industrial efficiency was calculated using the Moran’s Index model to ascertain whether a clustering effect exists within the Saudi Arabian construction industry. In conclusion, the aforementioned results are discussed, and recommendations for the sustainable development of the Saudi Arabian construction industry are provided.

2. Literature Review

2.1. Construction Industry Efficiency

Efficiency, as a widely used concept in production and business activities, has been given different connotations by academics, depending on the industry in which it is used; it is usually defined as the optimal relationship between inputs and outputs, aiming to achieve the maximum possible results with the least amount of resources [10]. Industrial efficiency is a measure of the ratio of the resources invested to the output achieved in a particular industry, focusing on productivity and resource optimization, and it involves a comprehensive analysis of a number of indicators for multi-input and multi-output industries [11]. Alsaleh et al. measured the cost, technical, and resource allocation efficiencies of the bioenergy industry of the EU-28 countries. They found that the capital cost, labor cost, GDP, inflation, and interest rate changes were important factors affecting the cost efficiency, while there were differences in the impact of the cost efficiency on the technical and resource allocation efficiencies [12]. Świtłyk et al. used data from the dairy cattle industry from the statistics of the relevant departments in Poland to evaluate the technical efficiency of the dairy cattle industry as well as the total factor production efficiency, and the study found that designing targeted policies based on technical efficiency measurements could contribute to improving the competitiveness of the Polish dairy industry [13]. Zakaria et al. discussed the factors affecting the development of the cement industry in Pakistan, analyzing the technical, pure technical, and scale efficiencies of the industry over the period of 2006–2016. It was found that the Gross Domestic Product (GDP) growth and exchange rate depreciation had a significant positive impact on the efficiency of the cement industry [14]. In conclusion, measuring the technical, scale, pure technical, and total factor production efficiencies are the main ways of measuring industrial efficiency.

In recent years, as an important part of the national economy, many scholars have carried out varied research on the industrial efficiency of the construction industry, especially on the trends and influencing factors in its efficiency, to analyze the current situation of the industry’s development and to provide a theoretical basis for the promotion of its efficient development. This study mainly adopts a non-parametric approach, data envelopment modeling (DEA), which, compared with a parametric approach, stochastic frontier analysis (SFA), does not need to pre-set the form of the production function or distributional assumptions and, thus, is able to flexibly adapt to different data distributions and complex production relationships. In addition, DEA is able to deal with multiple inputs and multiple outputs at the same time without the need to set up specific input–output relationships, which makes it more comprehensive in assessing efficiency [15]. Nazarko and Chodakowska, in order to analyze the impact of the economic crisis on the construction industry in Europe, applied the DEA–Malmquist model, which was based on the publicly available data from Eurostat for the period of 2006–2012 for 25 European countries, to measure the productivity of the construction industry. The study showed that there were large differences in productivity across Europe, and the analysis of the efficiency without considering the economic development of the country could lead to incorrect conclusions [16]. Li et al. conducted a macro-analytical study of the labor productivity of the U.S. construction industry, applying the DEA–Malmquist model to measure the change in the production efficiency from 2006 to 2016. The results of the study showed that the overall labor productivity of the industry during the sampling period had a downward trend, and there was a gap in the development of the industry in each state; however, the impact of random errors and the external environment on the production efficiency was not taken into account, which led to a lack of rigor in the results [17]. Yi used the DEA–Malmquist model to analyze the impact of China’s “One Belt, One Road” policy on the development of the construction industry in the provinces along the route and calculated the static productivity in each province using the statistical data of the construction industry in these provinces. The overall productivity of the construction industry was high; however, the overall efficiency of the construction industry had a downward trend, and the improvement in the technology level was the driver for the efficient development of the industry [18]. Yuan et al. evaluated the current situation of the development of China’s construction industry, applying the SE–DEA model to assess the technical, capital, equipment, and labor efficiency of the construction industry in 30 provinces in China. The technical and capital efficiency showed a relatively stable trend, and Beijing, Shanghai, and Zhejiang were the high-level development areas in the construction industry [19]. Wang et al. evaluated the efficiency of the construction industry in China’s Yangtze River Economic Belt based on the super-efficient SBM–Malmquist model, measuring the static and dynamic efficiencies of each province during the 18-year period of the sampling region. The efficiency in this construction industry experienced a changing trend from a gradual decline to a gradual improvement, and the changes in the efficiency were significantly affected by the degree of the industrial optimization and the level of industrialization [20]. Construction industry efficiency measurement involves systematic and long-term research work, and the choice of input–output indicators directly affects the evaluation results.

Table 1. Input and output variables of the construction industry obtained from the literature review.

References	Research Objects	Input Indicators	Output Indicators	Research Method
Nazarko and Chodakowska [16]	Construction industry in European countries	Number of employees	Total output value and total profit of the construction industry	DEA–Malmquist
Li et.al. [17]	Construction industry in America	Number of employees	Total output value of the construction industry	DEA–Malmquist
Yi [18]	Construction industry in provinces along the “Belt and Road”	Total enterprise assets, number of employees, and technical equipment rate	Total output value and total profit of the construction industry	DEA–Malmquist
Yuan et al. [19]	Construction industry in China	Number of employees, total enterprise assets, total capacity of machinery, and equipment owned	Gross product of the construction industry and newly built floor area	SE–DEA
Wang et al. [20]	Construction industry in China	Number of employees, year-end mechanical power, and total assets of the construction industry	Total output value of the construction industry and total construction area	SBM–Malmquist

summarizes the input and output indicators for measuring the efficiency of the construction industry based on the literature review of the construction industry.

2.2. Factors Influencing the Construction Industry’s Efficiency

The development of the construction industry is influenced by a number of factors, covering a wide range of fields such as economics, policy, and technology. Many scholars have already conducted in-depth studies on these influencing factors, proposing various theoretical and empirical analyses to reveal their specific roles and mechanisms in terms of the development of the construction industry. Asamoah et al. studied the influence of economic factors on the construction industry in Saudi Arabia, collecting the related research literature; a total of 59 economic factors were mentioned by scholars, of which GDP was most often mentioned, followed by inflation and the exchange rate [21]. Nazarko and Chodakowska studied the impact of the economic crisis on the construction industry in Europe, finding that the GDP was positively correlated with the productivity of the industry, and economic growth and the development of the industry had a mutually reinforcing effect [16]. Yang analyzed the factors affecting the efficiency of the construction industry in Anhui Province, finding that the increase in the area of the new construction and the GDP growth had a positive impact on the development of the industry, and the former had a higher degree of influence than the latter [22]. Ustinova and Sirazetdinov, in order to identify the factors influencing the development of the construction industry in Russia, compared the current situation of its development, the level of financial institutions, and so on, with other countries. The share of the total construction output value of the GDP, the new construction area, and the construction cost per square meter of new buildings had a significant impact on the level of development of the construction industry [23]. Wu studied the efficiency of the construction industry in Henan Province, considering carbon emissions, and analyzed how to improve the industrial efficiency of the construction industry in Henan Province with carbon emission constraints. The study showed that with the sustainable development of the construction industry, the carbon dioxide emissions were significantly reduced [24]. Shang et al. evaluated the industrial efficiency of China’s assembly building at both micro and macro levels and empirically analyzed the influencing factors. The study showed that the population size, gross construction output value, and carbon dioxide emissions had a significant impact on the efficiency of the construction industry at the macro level [25]. In this work, through a systematic review of the relevant literature on factors influencing the construction industry, we determined the main ideas and findings of the existing studies and refined the important indicators for evaluating the industrial efficiency, as shown in

Table 2. Factors influencing the construction industry obtained from the literature review.

References	Research Objects	Influencing Factors
Nazarko and Chodakowska [16]	Influencing factors in the European construction industry	GDP
Yang [22]	Influencing factors in the An Hui construction industry	GDP, new construction area, investment in construction and installation, expenditure on capital construction, and proportion of output in the tertiary industry
Ustinova and Sirazetdinov [23]	Industry and carbon efficiency in China	Proportion of the total output value of the construction industry of the GDP, new construction area, and construction cost per square meter of new buildings
Wu [24]	Relationship between industry efficiency and carbon emissions	Carbon emissions
Shang et al. [25]	Influencing factors in China’s prefabricated construction industry	Population scale, total output value of the construction industry, GDP, new real estate construction area, and carbon dioxide emissions

.

2.3. Spatial Autocorrelation

The industrial efficiency of the construction industry is not only affected by its own inputs and outputs but also by the development level of neighboring regions and the policy environment. LeSage and Pace developed a spatial autoregressive model, which captured the mutual influences and spillover effects among geographic units through the introduction of spatial proximity matrices [26]. Wang analyzed the changes in the efficiency of the construction industry in Beijing–Tianjin–Hebei and the characteristics of the spatial distribution of the efficiency in the region from a spatial and temporal two-dimensional perspective and found that the efficiency of the construction industry was distributed in a “high in the middle and low in the north and south” distribution, there was a positive spatial correlation for cities in Beijing–Tianjin–Hebei, and there was a significant phenomenon of the Matthew effect in the efficiency of the regional construction industry [27]. Chen et al. proposed a two-layer analysis framework of the coefficient of variation, Moran scatter plot, and convergence coefficient to analyze the trend of the construction TFP in the three major regions of China in terms of the spatial diversity, correlation, convergence, etc. The study found that the differences in the construction TFP among the regions were expanding, there were obvious spatial correlation and heterogeneity in the regional TFP, no relatively stable spatial pattern was formed, and the TFP among the three major regions showed a convergence effect [28].

2.4. Research Gap

Through the literature review, it was determined that a large number of scholars have already conducted in-depth research on the efficiency of the construction industry; however, the following shortcomings remain: (1) Radial DEA models have been widely used in the study of construction industry efficiency; yet, when dealing with efficiency assessment, they can only provide an estimate of the relative efficiency, ignoring the impact of slack variables on the results of the measurement. Although, the non-radial DEA model incorporates slack variables into the industrial efficiency evaluation, it lacks the flexibility of the parametric adjustment of inputs and outputs, which also affects the accuracy of the measurement results. (2) Static efficiency can only measure the industrial efficiency at a certain point in time, lacks in-depth analysis of the dynamic efficiency of the industry, and ignores the time dimension of the efficiency change and the dynamic characteristics of industrial development. (3) When studying the factors affecting industrial efficiency, linear regression analysis assumes that the independent variables are continuous and unrestricted, while construction industry efficiency data are usually truncated or restricted. In addition, the linear regression model assumes more stringent normality and homoskedasticity of the error term, and the actual data often do not satisfy these assumptions, which leads to the estimation of results, which is not robust and reduces the validity of the analysis of the influencing factors. (4) Although spatial autocorrelation analysis methods have been gradually applied to industrial efficiency research, spatial autocorrelation studies for the construction industry in Saudi Arabia are still insufficient, leading to the inability to identify whether there are spatial spillover effects of the construction industry in the region, which in turn affects the scientificity and effectiveness of policy formulation. This work proposes a scientific and complete industrial efficiency evaluation method to analyze the efficiency of Saudi Arabia’s construction industry using the super-efficient EBM model and the Malmquist index; in addition, it avoids the problem of estimating results when truncated data are analyzed for the factors affecting the efficiency of the construction industry, using the Tobit model, and it introduces the global Moran’s I to analyze the spatial autocorrelation of the construction industry, to comprehensively analyze the current situation of the development of the construction industry in Saudi Arabia in both the temporal and spatial dimensions.

3. Methodology

3.1. Model Development

3.1.1. Super-Efficient EBM Modeling

This study examined the industrial efficiency of the construction industry in Saudi Arabia. The term “industrial efficiency” refers to the degree of effectiveness and optimization of an industry in terms of input and output results [29]. It is also known as the efficiency of resource allocation in an industry [30]. In 1978, Charnes, Cooper, and Rhodes first proposed a data-driven non-parametric model to quantify the efficiency value of decision-making units (DMUs), thereby establishing a methodology for evaluating industrial efficiency [31]. The classical DEA models include the CCR model and BCC model, which assume constant returns to scale (CRS) and variable returns to scale (VRS), respectively. However, in practical problems, the reduction of input indicators and the expansion of output indicators do not occur in the same proportion, which may result in a biased outcome. Tone (2001) proposed a slack-based measure model (SBM), which is a non-radial model in which slack variables can overcome the bias in the results of traditional DEA models caused by the different proportions of changes in input and output indicators [32]. However, when studying the industrial efficiency of the construction industry in Saudi Arabia, it is important to consider the significant differences in the scale and complexity of the construction industry in different regions. Additionally, there is an imbalance in resource allocation, with some developed cities investing too much in the construction industry. To circumvent the impact of these issues on efficiency values, this work introduces the epsilon-based measure (EBM) model proposed by Tone et al. (2010). This model can not only consider the radial ratio of the target value to the actual value, but also simultaneously address radial and non-radial changes in the slackness between input and output factors, thereby enhancing the relative comparability of decision-making units [33]. Furthermore, to circumvent the issue of the meticulous examination of the disparity in efficacy between each valid evaluation unit when there are multiple valid DMUs and the efficacy value is 1, this study integrated the EBM with the super-efficiency model proposed by Andersen et al. (1993) [34] to develop the super-efficiency EBM model, thereby enhancing the efficacy of efficiency evaluation in the construction industry in Saudi Arabia. This model is shown in Equations (1) and (2):

$${\gamma }^{*}=min\theta -{\epsilon }_i{\sum }_{i=1}^n {\displaystyle \frac{w_i^{-}{s}_i^{-}}{x_{ik}}}$$

(1)

$$\mathrm{s}.1.\left\{\begin{array}{ll}\sum _{j=1}^n{X}_{ij}{\lambda }_j-\theta x_{ik}+s_i=0& i=\mathrm{1,2},\cdots ,m\\ & \\ \sum _{j=1}^n{y}_{ij}{A}_j\ge y_{ik}& r=\mathrm{1,2},\cdots ,s\\ & \\ A_j\ge 0,s_i^{-}\ge 0& \end{array}\right.$$

(2)

Assuming that the decision-making unit to be measured is the DMU, then, in Equation (1), ${\gamma }^{\mathrm{*}}$ is the optimal efficiency with variable returns to scale; $s_i$ is the input factor $i$ of the slack variable; $j$ is the decision-making unit; $n$ is the total number of decision-making units; $w_i^{-}$ is the weight of the input indicator that satisfies $\sum _{i=1}^m{w}_i^{-}=1$;${ X}_{ij}$ and $y_{ij}$ are the decision-making units $j$ of the decision unit $i$ inputs and $r$ type of outputs of the decision unit; $x_{ik}$ and $y_{ik}$ are the decision unit’s $k$ of the decision-making unit $i$ class of inputs and outputs, respectively, for the decision-making unit. $r$ represents the type of outputs of the decision module; $m$ and $s$ are the quantities of inputs and outputs, respectively; $\theta$ represents the planning parameters for the radial component; ${\lambda }_j$ is the linear combination coefficient; and ${\epsilon }_i$ is the key parameter, which satisfies 0 ≤ ${\epsilon }_i$ ≤ 1.

3.1.2. Malmquist Exponential Modeling

In order to study the dynamic efficiency of industries, Fare et al. combined the Malmquist index (MPI) proposed by Sten Malmquist in 1953 with the DEA theory and applied it to measure the efficiency of industries in the fields of finance, health care, agriculture, etc. [35]. The MPI index calculation calculates the change in the efficiency from the period t to the period t + 1, avoids the error caused by the arbitrariness of the selection of the period, and improves the identification ability of the model. The Malmquist index from period t to period t + 1 is shown in Equation (3):

$$\mathrm{M}\left({\mathrm{x}}^{\mathrm{t}},{\mathrm{y}}^{\mathrm{t}},{\mathrm{x}}^{\mathrm{t}+1},{\mathrm{y}}^{\mathrm{t}+1}\right)={\left({\mathrm{M}}_{\mathrm{t}}·{\mathrm{M}}_{\mathrm{t}+1}\right)}^{{\displaystyle \frac{1}{2}}}={\left({\displaystyle \frac{{\mathrm{D}}_{\mathrm{i}}^{\mathrm{t}}\left({\mathrm{x}}^{\mathrm{t}+1},{\mathrm{y}}^{\mathrm{t}+1}\right)}{{\mathrm{D}}_{\mathrm{i}}^{\mathrm{t}+1}\left({\mathrm{x}}^{\mathrm{t}+1},{\mathrm{y}}^{\mathrm{t}+1}\right)}}·{\displaystyle \frac{{\mathrm{D}}_{\mathrm{i}}^{\mathrm{t}}\left({\mathrm{x}}^{\mathrm{t}},{\mathrm{y}}^{\mathrm{t}}\right)}{{\mathrm{D}}_{\mathrm{i}}^{\mathrm{t}+1}\left({\mathrm{x}}^{\mathrm{t}},{\mathrm{y}}^{\mathrm{t}}\right)}}\right)}^{{\displaystyle \frac{1}{2}}}$$

(3)

The MPI index is composed of the technical progress (TC) and the technical efficiency change (EC). It can be expressed as Equation (4). When $MPI$ > 1, it means that the industrial efficiency has an upward trend, and the opposite has a downward trend. If $TC$ > 1, it indicates that the production technology has progressed; the opposite has a declining trend. If $EC$ > 1, it indicates that the technical efficiency has improved, and in the opposite, it is regressing.

$$\mathrm{M}\mathrm{P}\mathrm{I}=\mathrm{T}\mathrm{C}\times \mathrm{E}\mathrm{C}$$

(4)

3.1.3. Tobit Model

Traditional DEA models have efficiency values between 0 and 1, and although in the super-efficient EBM model, the efficiency value can exceed 1, the lower line of the efficiency rating remains at 0. In statistics, data that lie in a particular interval are end-of-tail data, and if least squares are used to estimate dependent variables with truncated-tail characteristics, the estimation results will be biased and inconsistent. Hence, Tobit regression is a model for restricted dependent variables that can effectively handle this type of data through the great likelihood estimation method. Assuming panel data for m decision units in period t, the expression for the panel Tobit model is shown in Equation (5):

$${\mathrm{y}}_{\mathrm{i}\mathrm{t}}=\left\{\begin{array}{ll}\mathsf{\alpha }+{\mathsf{\beta }}^{\mathrm{T}}{\mathrm{x}}_{\mathrm{i}\mathrm{t}}+{\mathrm{e}}_{\mathrm{i}\mathrm{t}},& {\mathsf{\alpha }}_{\mathrm{i}\mathrm{t}}+{\mathsf{\beta }}^{\mathrm{T}}{\mathrm{x}}_{\mathrm{i}\mathrm{t}}+{\mathrm{e}}_{\mathrm{i}\mathrm{t}}\ge 0\\ 0,& \mathrm{o}\mathrm{t}\mathrm{h}\mathrm{e}\mathrm{r}\end{array}\right.$$

(5)

where $i$ denotes the decision-making unit, $t$ is the period, $x_{it}$ is the independent variable, $y_{it}$ v is the dependent variable, $\alpha$ is the vector of constants, $\beta$ is the vector of coefficients, and $e$ is the error term of the normal distribution.

3.1.4. Spatial Autocorrelation

In spatial statistical analysis, the spatial autocorrelation method is the most commonly used indicator for measuring the geographic agglomeration of industries to measure the strength of neighboring physical associations, i.e., comparing the similarity of observations and the similarity between their spatial locations [36]. In order to measure the spatial autocorrelation of Saudi Arabia’s construction industry, this study introduced Moran’s Index (MI), reflecting the degree of similarity of the attribute values of spatially adjacent or spatially neighboring regional units. If $x$ is an observation, the global MI value for that variable is calculated using Equation (6):

$$\mathrm{M}\mathrm{I}={\displaystyle \frac{\sum _{\mathrm{i}=1}^{\mathrm{n}}\sum _{\mathrm{j}=1}^{\mathrm{n}}{\mathrm{w}}_{\mathrm{i}\mathrm{j}}\left({\mathrm{x}}_{\mathrm{i}}-\overline{\mathrm{x}}\right)\left({\mathrm{x}}_{\mathrm{j}}-\overline{\mathrm{x}}\right)}{{\mathrm{S}}^2\sum _{\mathrm{i}=1}^{\mathrm{n}}\sum _{\mathrm{j}=1}^{\mathrm{n}}{\mathrm{w}}_{\mathrm{i}\mathrm{j}}}}$$

(6)

where $w_{ij}$ is the spatial weight matrix in row$i$ of the $j$ column elements. Using a simple binary adjacency matrix, when the region $i$ and region $j$ are adjacent to each other, then $w_{ij}$ = 1. The MI is generally in the range of [−1, 1]: when MI < 0, this indicates that the two observation units are negatively correlated; when MI = 0, this indicates that the two observation units are uncorrelated; when MI > 0, this indicates that the two observation units are positively correlated.

3.2. Sample Size

In order to study the current status of the development of the construction industry in Saudi Arabia, this paper selected 13 provinces of Saudi Arabia as the research sample and empirically analyzed the data related to the construction industry in these 13 provinces for the decade from 2013 to 2022.

3.3. Types of Data

3.3.1. Industry Efficiency Indicators

This work evaluated the efficiency of the construction industry in terms of the regional industrial differences in Saudi Arabia, the level of resource inputs, and the efficiency of resource utilization, etc. Based on the literature review, this work selected the number of people employed in the construction industry and the number of construction enterprises as the input indexes and the gross output value of the construction industry and the efficiency of the construction industry’s labor production as the output indexes, as shown in

Table 3. Selection of measurement indices to determine the efficiency of the construction industry.

Index Name			Unit
Input index	I1	Number of construction industry employees	Person
	I2	Number of construction enterprises	set
Output index	O1	Gross output of the construction industry	SAR 100 million
	O2	Labor productivity of the building industry	SAR/person

, and the specific data are shown in

Table 4. The specific data of input and output indexes.

I1	2013	2014	2015	2016	2017	2018	2019	2020	2021	2022
Riyadh	911,450	948,690	959,324	981,106	756,851	677,508	568,420	566,108	577,289	581,466
Mecca	977,074	955,342	839,515	844,357	745,883	639,677	540,078	446,476	459,320	469,896
Eastern	975,510	749,757	769,010	513,297	441,228	421,819	440,675	421,379	430,147	428,364
Al Madinah	475,247	447,860	323,196	232,995	194,776	189,459	192,202	191,841	192,366	185,465
Asir	413,038	330,036	329,435	228,075	196,989	190,068	195,143	182,989	195,902	187,366
Jizan	298,645	261,502	253,414	168,790	144,724	139,244	142,446	139,758	144,950	140,292
Al-Qassim	276,546	273,357	242,170	170,482	159,432	147,122	147,954	143,822	147,648	141,067
Tabuk	177,575	189,560	189,385	116,296	105,534	97,388	101,525	99,957	104,445	99,423
Ha’il	161,806	169,930	168,461	107,986	89,411	86,578	90,854	88,911	91,352	90,542
Al Jawf	146,906	143,494	146,805	94,697	75,450	74,207	73,080	72,507	74,409	72,842
Najran	158,305	146,869	136,833	93,868	78,366	71,277	73,778	73,337	74,606	74,281
Northern Borders	98,457	98,715	90,416	62,154	48,327	47,332	52,357	49,927	49,374	48,329
Al Bahah	108,650	98,650	98,097	62,343	50,828	45,770	50,796	50,060	51,097	50,164
I2	2013	2014	2015	2016	2017	2018	2019	2020	2021	2022
Riyadh	2069	2275	2308	2923	2205	2076	2013	1927	2087	2228
Mecca	2874	2802	2569	2653	2298	2060	2146	1930	1982	2071
Eastern	2946	2383	2339	1884	1743	1845	2018	2085	2155	2064
Al Madinah	1076	1020	1192	1287	1016	1093	1102	1022	1166	1132
Asir	1053	1098	1129	1188	1196	1202	1235	1103	1159	1157
Jizan	752	781	775	866	734	701	729	707	743	750
Al-Qassim	736	741	785	857	809	815	827	801	834	840
Tabuk	528	602	611	631	560	523	612	588	587	585
Ha’il	453	529	527	537	552	507	541	514	508	526
Al Jawf	478	452	484	462	407	408	432	414	419	421
Najran	368	363	412	449	402	396	384	327	365	362
Northern Borders	325	369	361	296	245	234	269	237	247	256
Al Bahah	326	338	382	291	265	268	284	278	284	275
O1	2013	2014	2015	2016	2017	2018	2019	2020	2021	2022
Riyadh	31,541	37,470	38,806	42,639	43,299	42,290	43,436	42,611	44,603	45,104
Mecca	23,009	30,137	34,113	39,637	40,022	39,708	40,412	39,386	40,967	41,110
Eastern	16,740	20,666	23,943	25,928	26,223	27,814	29,804	27,242	30,624	32,900
Al Madinah	7987	8025	8937	10,572	11,250	11,084	11,913	10,605	11,902	12,224
Asir	8118	9260	9921	9604	9678	9750	10,214	9909	10,604	10,627
Jizan	5817	5912	6256	6803	7022	7176	7159	6932	7393	7376
Al-Qassim	5685	5742	5880	6328	6417	6431	6860	6628	7033	7014
Tabuk	3525	3726	4014	4179	4287	4384	4467	4333	4642	4651
Ha’il	2934	3057	3203	3497	3424	3622	3746	3643	3903	3918
Al Jawf	2432	2770	2656	2854	2885	2979	3034	2932	3137	3128
Najran	2362	2399	2409	2815	2851	2836	2984	2911	3096	3209
Northern Borders	1088	1200	1219	1561	1723	1724	1886	1829	1961	1961
Al Bahah	1430	1620	1626	1660	1671	1698	1738	1676	1789	1881
O2	2013	2014	2015	2016	2017	2018	2019	2020	2021	2022
Riyadh	0.3292	0.5226	0.7994	1.0918	1.3832	1.3953	1.5283	1.5054	1.5453	1.5514
Mecca	0.1727	0.2362	0.3129	0.6660	0.7723	0.8756	0.9333	1.0230	1.0408	1.0455
Eastern	0.1159	0.1263	0.3579	0.4857	0.5490	0.5646	0.5856	0.5719	0.6190	0.8164
Al Madinah	0.0423	0.0604	0.1804	0.2269	0.2368	0.2395	0.2555	0.2764	0.2912	0.3026
Asir	0.0620	0.0868	0.1677	0.2274	0.2571	0.2629	0.2826	0.2773	0.2923	0.3063
Jizan	0.0225	0.0286	0.0907	0.1209	0.1373	0.1417	0.1508	0.1488	0.1530	0.1577
Al-Qassim	0.0231	0.0397	0.0830	0.1089	0.1127	0.1205	0.1298	0.1290	0.1334	0.1392
Tabuk	0.0210	0.0329	0.0569	0.0983	0.0968	0.1048	0.1100	0.1084	0.1111	0.1170
Ha’il	0.0207	0.0252	0.0471	0.0712	0.0842	0.0870	0.0907	0.0901	0.0940	0.0952
Al Jawf	0.0186	0.0237	0.0322	0.0482	0.0591	0.0599	0.0664	0.0647	0.0675	0.0687
Najran	0.0168	0.0184	0.0286	0.0390	0.0456	0.0499	0.0526	0.0516	0.0540	0.0628
Northern Borders	0.0048	0.0072	0.0141	0.0198	0.0235	0.0260	0.0252	0.0247	0.0318	0.0365
Al Bahah	0.0047	0.0066	0.0123	0.0186	0.0218	0.0234	0.0239	0.0234	0.0280	0.0319

.

The number of persons employed in construction reflects the use of human resources in the industry and is a key indicator of the efficiency of labor supply and allocation. A high level of employment usually means more labor input, which is particularly important for the labor-intensive construction industry. Meanwhile, the number of construction firms reflects the size and degree of competition among market participants in the industry. A larger number of firms usually implies greater market activity and diversity in the allocation of resources, which helps to promote innovation and efficiency in the industry. As output indicators, the choice of gross output value of construction and labor productivity of construction is also based on scientific considerations. The gross output value of the construction industry is a comprehensive indicator of the industry’s economic contribution and market value, which can visually reflect the production results and economic benefits of the industry as a whole, and the higher its value, the greater the industry’s contribution to the national economy. On the other hand, the labor productivity of the construction industry is an important indicator for evaluating the production capacity of a unit of labor, which reflects the effect of labor utilization and productivity level. High labor productivity means that more output can be obtained from the same input, thus increasing the overall efficiency of the industry.

Therefore, the selected output indicators can be used to comprehensively and systematically assess the efficiency performance of the construction industry in Saudi Arabia. Such an indicator system can not only reveal the actual situation of resource allocation and utilization in the industry, but also provide a scientific basis for optimizing the industrial structure, enhancing market competitiveness, and achieving sustainable development.

3.3.2. Indicators of Impact Factors

This study identified population, GDP, new construction area, and carbon dioxide emissions as factors affecting the construction industry in Saudi Arabia from the perspective of sustainable development. These factors were selected based on a literature review that identified them as influencing the three dimensions of economic, social, and environmental benefits.

Firstly, population size is directly correlated with the availability of a labor force and market demand, and is therefore an important indicator of the potential for growth within the construction industry. A larger population results in a greater availability of labor resources and an increased demand for housing, which in turn stimulate the growth of the construction industry. Secondly, GDP serves as an indicator of the prevailing economic situation and macroeconomic environment, directly influencing investment and development within the construction industry. A high GDP is typically accompanied by elevated financial investment and infrastructure construction requirements, which serve to advance the prosperity of the construction industry. The new construction area represents the scale of new projects in the industry and the level of construction activity. It is a core indicator for measuring the growth of the construction industry and market dynamics. The expansion of new construction areas is indicative of the growth and prosperity of construction activities, which in turn enhances the efficiency and output level of the industry. Ultimately, carbon dioxide emissions, as an environmental impact factor, are directly correlated with the sustainable development and environmental protection requirements of the construction industry. Elevated carbon dioxide emissions may indicate deficiencies in energy conservation and emission reduction within the industry, underscoring the necessity for enhanced production and construction technology to achieve green development. Consequently, the aforementioned indicators have been identified as the influencing factors of the construction industry in Saudi Arabia.

3.4. Method of Data Collection

The principal data sources for this study are the website of the Saudi Arabia National Bureau of Statistics (http://stats.gov.sa, accessed on 14 December 2023) and the Saudi Arabia Construction Statistics Yearbook.

4. Results

4.1. Static Efficiency Results for the Construction Industry in Saudi Arabia

4.1.1. Analysis of the Technical Efficiency (TE) Results

This work used MAXDEA9.0 to realize the super-efficient EBM model to measure the efficiency of Saudi Arabia’s construction industry, and the results are shown in

Table 5. Technical efficiency of the construction industry in Saudi Arabia (2013–2022).

Area	2013	2014	2015	2016	2017	2018	2019	2020	2021	2022	Average
Riyadh	0.670	0.727	0.742	0.653	0.877	0.914	1.001	1.002	1.000	1.000	0.859
Mecca	0.358	0.481	0.595	0.671	0.781	0.868	0.891	0.997	1.011	0.980	0.763
Eastern	0.255	0.390	0.459	0.637	0.711	0.738	0.755	0.717	0.789	0.854	0.630
Al Madinah	0.326	0.346	0.347	0.499	0.638	0.642	0.681	0.616	0.677	0.721	0.549
Asir	0.341	0.381	0.398	0.465	0.547	0.574	0.592	0.617	0.613	0.653	0.518
Jizan	0.342	0.339	0.362	0.446	0.537	0.571	0.556	0.549	0.564	0.580	0.485
Al-Qassim	0.343	0.345	0.337	0.411	0.446	0.481	0.510	0.507	0.524	0.545	0.445
Tabuk	0.299	0.278	0.296	0.396	0.448	0.496	0.482	0.475	0.489	0.513	0.417
Ha’il	0.289	0.259	0.273	0.359	0.419	0.459	0.452	0.450	0.470	0.475	0.390
Al Jawf	0.229	0.275	0.248	0.335	0.422	0.442	0.455	0.444	0.463	0.471	0.378
Najran	0.283	0.292	0.262	0.333	0.402	0.438	0.447	0.444	0.461	0.480	0.384
Northern Borders	0.151	0.151	0.160	0.279	0.395	0.404	0.399	0.408	0.440	0.448	0.324
Al Bahah	0.196	0.217	0.200	0.297	0.363	0.407	0.376	0.368	0.385	0.413	0.322
Annual average	0.314	0.345	0.360	0.445	0.538	0.572	0.584	0.584	0.607	0.626	0.497

. To specifically evaluate the current status of Saudi Arabia’s construction industry development based on the results of the industrial efficiency measurements, this work drew on the three-level evaluation study of the technical efficiency of power generation in China’s provinces by Wu et al. (2018) to differentiate the current status of the industry’s development, which stated that TE ≥ 0.9 means the industry is at a high level of development, 0.6 ≤ TE < 0.9 means the industry is at a medium level of development, and TE ≤ 0.6 means the industry is at a low level of development [37].

From the results, it can be seen that over time, the technical efficiency of the construction in Saudi Arabia showed an upward trend (see Figure 1), increasing from 0.314 in 2013 to 0.626 in 2022, which was an increase of 49.79%, with its highest growth rate in 2016, of 19.07% compared to 2015. However, the average annual efficiency value was below 0.6 until 2020, which indicates that the construction industry in Saudi Arabia was at a low level of development; however, the overall development was on an upward trend, especially after 2020, when it reached a medium level of development. In the spatial dimension, the average value of the technical efficiency of the construction industry in Riyadh, Mecca, and Eastern was more than 0.6, and the industry was at a medium level of development. This is noted by the fact that Riyadh had an efficiency value higher than 0.9 after 2018 and an efficiency value greater than or equal to 1 for effective DMUs from 2019 to 2022, which indicates that the construction industry in this region reached a high level of development after 2018. The construction industry in Riyadh is the most developed among the provinces of Saudi Arabia, primarily due to its status as the capital and economic center of the country. This confers upon it greater access to resources and investment opportunities. The substantial financial backing and elevated investment intensity have markedly accelerated the pace of development within the construction industry. While Mecca and the eastern region do not possess the same advantages as Riyadh, the holy city of Islam attracts a considerable number of pilgrims on an annual basis, thereby stimulating the growth of associated infrastructure and construction projects. In contrast, the eastern region has emerged as a pivotal hub for the advancement of the construction industry, largely due to its abundant oil reserves and the robust growth of the oil sector. Furthermore, the eastern region boasts numerous significant ports, and the concomitant logistics industry has necessitated a considerable amount of infrastructure construction, which has also served to advance the development of the construction industry. Consequently, the degree of advancement of the construction industry in these three provinces is markedly superior to that observed in other provinces.

Al Madinah and Asir had an efficiency value of more than 0.6 after 2017 and 2020, respectively, and the construction industry reached a medium level of development. The construction of a series of large-scale projects in Medina and Asir, two of Saudi Arabia’s most prominent tourist destinations, has contributed to this growth. Notable examples include the expansion of the Prophet’s Mosque in Medina, the Medina Economic City, and the Mount Sauda Tourist Area in Asir. The aforementioned projects have not only led to a notable enhancement of the regional infrastructure and urban services, but have also attracted a considerable amount of investment, facilitated economic diversification, and promoted tourism development. These factors have collectively led to a notable advancement in the developmental status of the construction industry in Medina and Asir in the period following 2017. The other governorates had efficiency values below 0.6 until 2022 despite the upward trend in industrial efficiency, and the construction industry remained at a low level of development.

4.1.2. Analysis of the Pure Technical Efficiency (PTE) Results

The results of the pure technical efficiency of the construction industry in Saudi Arabia are shown in

Table 6. The pure technical efficiency of the construction industry in Saudi Arabia (2013–2022).

Area	2013	2014	2015	2016	2017	2018	2019	2020	2021	2022	Average
Riyadh	0.684	0.730	0.743	0.651	0.884	0.912	1.003	1.003	1.005	1.000	0.862
Mecca	0.381	0.495	0.606	0.674	0.784	0.872	0.893	1.000	1.006	0.984	0.770
Eastern	0.285	0.423	0.486	0.666	0.742	0.760	0.771	0.738	0.803	0.865	0.654
Al Madinah	0.438	0.463	0.445	0.586	0.741	0.746	0.780	0.731	0.775	0.823	0.653
Asir	0.456	0.490	0.502	0.558	0.660	0.690	0.703	0.738	0.724	0.768	0.629
Jizan	0.514	0.506	0.529	0.584	0.697	0.737	0.718	0.716	0.722	0.742	0.646
Al-Qassim	0.520	0.520	0.506	0.550	0.594	0.639	0.665	0.667	0.677	0.704	0.604
Tabuk	0.563	0.510	0.523	0.617	0.686	0.762	0.737	0.739	0.732	0.777	0.665
Ha’il	0.600	0.528	0.541	0.599	0.718	0.764	0.742	0.752	0.759	0.766	0.677
Al Jawf	0.532	0.592	0.545	0.621	0.765	0.790	0.809	0.806	0.809	0.827	0.710
Najran	0.668	0.683	0.611	0.628	0.736	0.801	0.799	0.863	0.819	0.839	0.745
Northern Borders	0.695	0.618	0.635	0.788	0.975	1.025	0.920	1.001	1.000	1.005	0.866
Al Bahah	0.688	0.670	0.599	0.799	0.922	1.004	0.914	0.924	0.916	0.947	0.838
Annual average	0.540	0.556	0.559	0.640	0.762	0.808	0.804	0.821	0.827	0.850	0.717

. Its development trend is shown in Figure 1; the pure technical efficiency was on an upward trend, reaching 0.850 in 2022 from 0.540 in 2013, especially in 2016 and 2017, where the increase over the previous year amounted to 12.63% and 15.97%, respectively, and reached a peak in 2022 of 0.850. There was technical inefficiency; i.e., the factors of production were not fully utilized. From the spatial perspective, the pure technical efficiency of the Riyadh, Northern, and Al Bahah governorates exceeded 0.8, which was significantly higher than the other governorates. As remote areas of Saudi Arabia, the Northern Border Province and Al Bahah Province have been the recipients of numerous government support policies, including the “Technology Innovation and Introduction Support Program” and the “Vocational Training and Education Development Program”. These policies not only prompt construction companies to adopt and utilize sophisticated construction technologies and equipment, but also effectively enhance the technical proficiency of the local workforce. Consequently, these two provinces are at the vanguard of the construction industry in terms of pure technical efficiency. However, the annual average construction industry efficiency in Northern and Al Bahah was very low at 0.324 and 0.322, respectively, which explains the fact that despite the high utilization of resources, they did not reach the optimal scale of production in the construction industry, and there were diseconomies of scale.

4.1.3. Analysis of the Scale Efficiency (SE) Results

The results of the scale efficiency in the construction industry in Saudi Arabia are shown in

Table 7. The scale efficiency of the construction industry in Saudi Arabia (2013–2022).

Area	2013	2014	2015	2016	2017	2018	2019	2020	2021	2022	Average	Returns to Scale
Riyadh	0.980	0.996	0.999	1.002	0.993	1.002	0.997	0.999	0.995	1.000	0.996	-
Mecca	0.939	0.971	0.983	0.996	0.997	0.995	0.998	0.997	1.005	0.996	0.988	-
Eastern	0.892	0.923	0.943	0.956	0.958	0.971	0.978	0.972	0.982	0.987	0.956	irs
Al Madinah	0.746	0.747	0.781	0.852	0.862	0.861	0.873	0.843	0.874	0.877	0.831	irs
Asir	0.749	0.777	0.793	0.834	0.829	0.832	0.842	0.836	0.847	0.851	0.819	irs
Jizan	0.666	0.669	0.684	0.764	0.771	0.775	0.775	0.768	0.782	0.782	0.743	irs
Al-Qassim	0.660	0.662	0.667	0.747	0.751	0.753	0.767	0.760	0.773	0.773	0.731	irs
Tabuk	0.531	0.546	0.566	0.641	0.654	0.652	0.654	0.643	0.667	0.660	0.621	irs
Ha’il	0.481	0.492	0.505	0.599	0.583	0.601	0.609	0.598	0.620	0.620	0.571	irs
Al Jawf	0.431	0.465	0.454	0.539	0.551	0.560	0.562	0.551	0.573	0.570	0.526	irs
Najran	0.423	0.427	0.429	0.531	0.547	0.547	0.559	0.514	0.562	0.572	0.511	irs
Northern Borders	0.217	0.245	0.252	0.355	0.405	0.394	0.433	0.407	0.440	0.446	0.359	irs
Al Bahah	0.285	0.324	0.334	0.371	0.394	0.405	0.412	0.399	0.421	0.436	0.378	irs
Annual average	0.615	0.634	0.645	0.707	0.715	0.719	0.728	0.714	0.734	0.736	0.695	irs

Note: irs indicates returns to scale is increasing.

, with a general upward trend from 2013 to 2022, despite the fluctuating changes in the scale efficiency. The largest increase in scale efficiency was observed in 2016, from 0.645 in 2015 to 0.707 in 2016 (an increase of 8.69%); however, the scale efficiency values were all less than 1 and did not reach scale efficiency effectiveness. In addition, from a spatial perspective, only two provinces, Riyadh and Mecca, had constant returns to scale, while the rest had increasing returns to scale (irs), which indicates that the current construction industry in these provinces was small in scale and that there is a potential for a substantial increase in the efficiency of the industry as the industry expands, to achieve the efficient development of the industry. As the primary economic and religious hubs of Saudi Arabia, Riyadh and Mecca have developed comprehensive infrastructure and well-established construction markets. These two provinces attract a considerable amount of investment and a highly skilled labor force, and the development of the construction industry is characterized by stability. Consequently, the production efficiency and resource allocation efficiency of construction enterprises in these regions have reached a relatively high level, and the marginal contribution of new inputs to output is relatively stable. Moreover, the market demand in Riyadh and Mecca is relatively stable and substantial, enabling construction enterprises to accurately gauge market demand and formulate corresponding production plans and resource allocations. This stability enables companies to maintain consistent production efficiency when expanding production scale, thereby exhibiting the characteristics of constant returns to scale. In contrast, the construction market in other provinces is still in a phase of rapid development. In contrast to the more mature and developed industrial bases of Riyadh and Mecca, where market demand is also growing rapidly, the returns to scale in other provinces are still in a state of flux.

4.2. Dynamic Efficiency Results for the Construction Industry in Saudi Arabia

In this work, MAXDEA 9.0 was used to measure the Malmquist index of Saudi Arabia’s construction industry, to evaluate its dynamic efficiency, and the results are presented in

Table 8. Malmquist index of the construction industry in Saudi Arabia (2013–2022).

Year	Technical Efficiency Change (EC)	Technological Change (TC)	Total Factor Productivity Change (TFP)
2013–2014	1.041	1.059	1.124
2014–2015	1.021	1.043	1.059
2015–2016	1.228	1.063	1.293
2016–2017	1.058	1.150	1.210
2017–2018	0.987	1.081	1.069
2018–2019	0.918	1.109	1.015
2019–2020	0.834	1.201	1.006
2020–2021	1.031	1.011	1.040
2021–2022	1.052	0.984	1.029
Average annual rate of change	1.019	1.078	1.094

. The annual average values of the total factor productivity (TFP), technical efficiency changes (EC), and technological advancement (TC) were all greater than 1, which indicates that the industry’s performance has improved across the board. The annual average value of the change in the TFP efficiency was 1.094, reaching a peak of 1.293 in 2015–2016, which indicates a gradual improvement in the efficiency of the industry, as the output capacity of the construction industry in Saudi Arabia increased under the given input conditions [38]. However, the TFP change showed a decreasing trend during the sampling period, which indicates that although the industrial efficiency of the Saudi Arabian construction industry is generally improving, the increase in efficiency gradually decreased each year. The annual average of the EC change was 1.019, which indicates that the construction industry has become more efficient in terms of resource utilization during the sampling period, with better management practices and optimization of the efficiency in the allocation of resources. However, the value of the EC change was less than 1 in 2017–2020 and reached a low of 0.834 in 2019–2020, which indicates that the resource allocation efficiency failed during 2017–2020, and it is possible that the Saudi Arabian government could analyze the wastefulness of resource inputs in the construction industry during this time period and optimize the market structure by improving the policy and regulatory environment to achieve efficient development [39].

In addition, the annual average value of the TC was 1.078, and the change in the TC was greater than 1 in the sampling period, reaching a peak of 1.201 in 2019–2020; thereafter, the overall growth was on a downward trend. This indicates that the overall level of technology increased during the sampling period; however, the rate of technological progress gradually slowed. It is possible that this phenomenon is due to the fact that at the beginning of the sampling period, the large-scale introduction and application of advanced technology had a technology diffusion effect, which led to a rapid increase in the level of technology [40]. As the level of technology increases, the phenomenon of diminishing marginal benefits gradually appears, leading to the increasing difficulty and cost of further upgrading the level of technology, thus slowing down the rate of technological progress [41]. It is worth noting that during the sampling period, the TFP, TC, and EC all showed a fluctuating trend of change, while the trend of the TFP and EC change was the same, which indicates that resource allocation efficiency changes drive the fluctuation in the TFP. This may be due to a combination of factors, such as changes in management practices and operational efficiency, the impact of market demand and economic cycles, the impact of changes in policies and regulations, the volatility of technological advances, and training and skill development, leading to a more pronounced impact of the EC on the TFP [42].

4.3. Results for the Factors Affecting the Construction Industry in Saudi Arabia

Based on the influencing factors selected in 3.4.2, the Tobit model constructed in this work is shown in Equations (7) and (8), where $\mathrm{E}{\mathrm{F}}_{\mathrm{i}\mathrm{t}}$ is the industrial efficiency of the construction industry in 13 provinces of Saudi Arabia from 2013 to 2022; $\mathrm{l}\mathrm{n}\mathrm{P}\mathrm{O}{\mathrm{P}}_{\mathrm{i}\mathrm{t}}$ denotes the population size; ${\mathsf{\alpha }}_2\mathrm{l}\mathrm{n}\mathrm{G}\mathrm{D}{\mathrm{P}}_{\mathrm{i}\mathrm{t}}$ is the gross national product; $\mathrm{l}\mathrm{n}\mathrm{N}\mathrm{C}{\mathrm{A}}_{\mathrm{i}\mathrm{t}}$ denotes the area of new real estate projects; $\mathrm{l}\mathrm{n}\mathrm{C}\mathrm{D}{\mathrm{E}}_{\mathrm{i}\mathrm{t}}$ denotes carbon dioxide emissions; ε is a random perturbation term$; \mathrm{i}$ denotes the province; and $\mathrm{t}$ denotes the year. According to the model, Stata software 16 was used to analyze the influence factors empirically. The results are shown in

Table 9. Factors influencing the efficiency of the construction industry in Saudi Arabia.

Variable Name	Coefficient	Standard Deviation	t	p Value
$\mathrm{l}\mathrm{n}\mathrm{P}\mathrm{O}{\mathrm{P}}_{\mathrm{i}\mathrm{t}}$	−1.287172	0.5432381	−2.37	0.020 ***
$\mathrm{n}\mathrm{G}\mathrm{D}{\mathrm{P}}_{\mathrm{i}\mathrm{t}}$	1.042445	0.2616942	3.98	0.000 ***
$\mathrm{l}\mathrm{n}\mathrm{N}\mathrm{C}{\mathrm{A}}_{\mathrm{i}\mathrm{t}}$	−0.0364423	0.0117636	−3.1	0.002 ***
$\mathrm{l}\mathrm{n}\mathrm{C}\mathrm{D}{\mathrm{E}}_{\mathrm{i}\mathrm{t}}$	−0.926155	0.1229921	−7.53	0.000 ***

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

.

$$\mathrm{E}{\mathrm{F}}_{\mathrm{i}\mathrm{t}}=\mathsf{\beta }+{\mathsf{\alpha }}_1\mathrm{l}\mathrm{n}\mathrm{P}\mathrm{O}{\mathrm{P}}_{\mathrm{i}\mathrm{t}}+{\mathsf{\alpha }}_2\mathrm{l}\mathrm{n}\mathrm{G}\mathrm{D}{\mathrm{P}}_{\mathrm{i}\mathrm{t}}+{\mathsf{\alpha }}_3\mathrm{l}\mathrm{n}\mathrm{N}\mathrm{C}{\mathrm{A}}_{\mathrm{i}\mathrm{t}}+{\mathsf{\alpha }}_4\mathrm{l}\mathrm{n}\mathrm{C}\mathrm{D}{\mathrm{E}}_{\mathrm{i}\mathrm{t}}+{\mathsf{\epsilon }}_{\mathrm{i}\mathrm{t}}$$

(7)

$$\mathrm{i}=\mathrm{1,2},······,13;\mathrm{t}=\mathrm{1,2},······10$$

(8)

The results show that there was a significant difference in the impact of each influencing factor on the industrial efficiency. Specifically, population had a significant negative correlation on the efficiency of the construction industry, with a regression coefficient of −1.287172, indicating that an increase in the population may bring about the saturation of the labor market and the intensification of the competition for resources, thus reducing the overall efficiency. With the increase in population size, the supply and demand in the labor market gradually becomes imbalanced. In the construction industry, labor is one of the indispensable factors of production. However, when the population surges, the supply in the labor market increases rapidly, while the demand fails to grow in tandem. This leads to the saturation phenomenon in the labor market, and a large number of laborers face fierce competition when looking for jobs, thus limiting the effective use of labor within the industry and leading to a decrease in the efficiency of the construction industry [43]. In addition, the increase in population has also intensified the fierce competition for land resources; the limited nature of land resources has made land for construction increasingly scarce, leading to rising land prices; the price of housing has also risen, and these increased costs undoubtedly have a negative impact on the efficiency of the construction industry [44]. In order to achieve rapid profitability, construction companies often rapidly expand their market share, thereby failing to recognize the importance of technological innovation. This ultimately limits the transformation of the construction industry from a labor-intensive industry to a technology-intensive industry, which in turn hinders the efficient development of the industry.

The GDP had a significant positive correlation with industrial efficiency, and its regression coefficient was 1.042445, indicating that economic growth can significantly promote the efficiency of the construction industry. GDP, as an important indicator to measure the final results of the production activities of all resident units of a country in a certain period of time, reflects the country’s economic strength and the level of economic development [45]. With the development of the economy and the increase in the level of GDP, the government and enterprises obtain more funds for investment, which directly promotes the rapid development of the construction industry [46]. In addition, the growth of the GDP also promotes the development of technological innovation. In the context of economic growth, people’s pursuit of quality of life is increasing, prompting enterprises to continuously innovate to meet market demand. In the construction industry, the application of new materials, processes, and technologies has made the process of building design and construction more scientific and efficient, which has significantly improved the industrial efficiency [47].

The area of new real estate projects also had a significant negative correlation on the industrial efficiency; however, its regression coefficient was only −0.00364423, the lowest regression coefficient among the influencing factors, which suggests that, although an increase in the area of new real estate projects may bring about a certain amount of resource dispersion and management burden, its negative impact on the efficiency of the Saudi Arabian construction industry is relatively weak. This may be due to the fact that an increase in the size of new real estate projects often implies a reallocation of resources and an increase in management difficulties. In the short term, it may lead to a dispersal of resources and increase in management costs of the original projects, thus reducing the industrial efficiency to some extent [48].

Carbon dioxide emissions had a significant negative impact on the efficiency of the construction industry, with a regression coefficient of −0.926155. Carbon dioxide emissions may be decreasing as the efficiency of the industry improves. In the process of improving the efficiency of the construction industry, energy consumption and pollution emissions are reduced through the use of green building technologies, energy-efficient materials, and optimized construction management, thus reducing CO₂ emissions [49]. This not only improves the quality of the environment but also promotes the trend of the construction industry in Saudi Arabia to transition to green and sustainable development.

4.4. Spatial Autocorrelation Results for the Efficiency of the Construction Industry in Saudi Arabia

Based on the spatial autocorrelation formula,

Table 10. Moran’s I of the construction industry efficiency in Saudi Arabia.

Variables	I	E(I)	Sd(I)	Z	p Value
2013	0.093	−0.083	0.125	1.417	0.078
2014	0.178	−0.083	0.138	1.900	0.029
2015	0.147	−0.083	0.153	1.511	0.065
2016	0.090	−0.083	0.166	1.044	0.097
2017	0.113	−0.083	0.162	1.211	0.095
2018	0.118	−0.083	0.161	1.245	0.093
2019	0.148	−0.083	0.159	1.453	0.073
2020	0.140	−0.083	0.157	1.423	0.077
2021	0.130	−0.083	0.160	1.340	0.090
2022	0.149	−0.083	0.163	1.419	0.078

presents the global Moran’s I results of Saudi Arabia’s construction industry efficiency from 2013 to 2022. The results show that the Moran’s I of Saudi Arabia’s construction industry efficiency from 2013 to 2022 was positive, and the p value was less than 0.1, which passed the test of significance, indicating that there was a significant positive correlation and that Saudi Arabia’s construction industry efficiency showed a certain spatial clustering trend. However, the overall relatively low value of the Moran’s I indicates that this spatial autocorrelation was not strong, and there was no significant upward or downward trend, showing a degree of stability. It is possible that this phenomenon is due to significant differences in the level of economic development, investment in infrastructure development, policy support, diffusion of technological innovations, and resource allocation among the provinces of Saudi Arabia [50]. These differences are formed over a long period of time and are difficult to change substantially in a short period of time, which leads to the spatial autocorrelation of the efficiency of the Saudi Arabian construction industry remaining relatively stable over a long period of time in spite of some spatial clustering phenomena in different regions.

5. Discussion

In this work, the current status of the construction industry development in Saudi Arabia was studied in terms of the industry static and dynamic efficiency, the factors affecting the industry efficiency, and spatial autocorrelation of the industry efficiency. In terms of the industrial static efficiency, although the technical efficiency of Saudi Arabia’s construction industry showed an upward trend and significant growth during the sampling period, the efficiency values were low, and only the efficiency values of 2021 and 2022 were more than 0.6, which indicates that the overall development of Saudi Arabia’s construction industry remains at a low level. The pure technical efficiency was less than 1 in all cases, which indicates that the Saudi Arabian construction industry underutilized its established resources and technology during the sampling period, and there was a certain degree of technical inefficiency. The scale efficiency was less than 1, and except for Riyadh and Mecca, where the returns to scale were unchanged, the rest of the provinces showed increasing returns to scale (irs), which indicates that the Saudi Arabian construction industry fails to fully utilize the resources and production capacity at the existing scale, it does not realize optimal economies of scale, and the efficiency of the industry can be further improved by expanding the scale of production. To address the above problems, Saudi Arabia should vigorously promote technological innovation and upgrading through the introduction and research and development of advanced construction technologies, such as building information modeling (BIM), digital twin (DT), and assembly construction, to avoid the waste of resources, improve the productivity of the construction industry, and achieve the sustainable development of the construction industry [51,52]. In addition, the Saudi Arabian government should promote the expansion of the industrial scale, promote the centralized allocation of resources and production factors through optimizing the industrial layout, and form an industrial agglomeration effect, to encourage the construction industry in Saudi Arabia to achieve optimal economies of scale and improve the competitiveness of the construction industry [53].

In terms of the dynamic efficiency of the industry, the changes in the technical efficiency, technological progress, and total factor production efficiency were all greater than 1, and the total factor production efficiency had the same trend as the change in technical efficiency. This means that the technical efficiency and technology level of Saudi Arabia’s construction improved during the sampling period, the application of new technologies promoted progress in the industry, and the optimization of resource allocation is the main driving factor for the improvement in the total factor production efficiency. After 2020, technological progress showed a declining trend, reflecting some challenges in technological innovation in the Saudi Arabian construction industry. This is due to the fact that in response to the threat posed by the COVID-19 epidemic to construction workers, Saudi Arabian construction firms accelerated their digital transformation by widely applying cutting-edge technologies, such as telecommuting, intelligent monitoring and control management systems, and construction robots, which accelerated the technological advancement in the construction industry during this period [54]. However, as the cost of technological upgrading increased, the phenomenon of diminishing marginal effect emerged, and the uncertainty in the global economy due to the COVID-19 epidemic reduced the rate of the technological advancement. The Saudi Arabian government should optimize policy support, formulate industrial policies conducive to technological innovation, and encourage enterprises to carry out technological transformation and upgrading. At the same time, it should strengthen international advanced technology exchanges, accelerate international technology diffusion, and realize the reduction in technological upgrading costs, to reverse the declining trend in technological change and sustainably enhance the long-term competitiveness of the industry.

In addition, this work analyzed the factors influencing the efficiency of the construction industry in Saudi Arabia in terms of its economic, social, and environmental benefits. Population growth has the greatest negative impact on the industrial efficiency, GDP enhancement has a significant and positive impact on the industrial efficiency, and the new construction area has a negative but insignificant impact on the industrial efficiency. This indicates that population growth leads to the saturation of the labor market, and increased competition for resources limits the efficient development of the industry, while GDP growth can promote the development of new technologies and accelerate the transformation of the construction industry from a labor-intensive industry to a resource-intensive industry. The Saudi Arabian government should pay attention to the training of technicians in the construction industry to continuously improve the productivity of the construction industry and realize the improvement in industrial efficiency [55]. It is worth noting that CO₂ emissions have a significant negative impact on industrial efficiency. However, the efficiency of the construction industry continued to improve during the sampling period, which means that CO₂ emissions are gradually decreasing. This demonstrates that the Saudi Arabian construction industry contributes to carbon emission reduction, and the industry is moving towards green sustainability [56].

By calculating the global Moran’s I for the construction industry in Saudi Arabia, it was found that there is a spillover effect of the construction industry in space; however, the spatial autocorrelation is weak, and the trend of change is not obvious during the sampling period. This indicates that there is some mutual influence on the development of the construction industry in different regions of Saudi Arabia; however, this spatial correlation is not effective and fails to form an obvious clustering effect. In order to further enhance the spatial spillover effect of the construction industry, Saudi Arabia should enhance inter-regional synergistic development, optimize regional policies, promote resource sharing and technology diffusion, strengthen inter-regional cooperation and exchanges, and encourage the construction industry to achieve efficient and balanced development on a wider scale. Through the development and implementation of more comprehensive regional policies, the optimal allocation of resources and the widespread dissemination of technology can be promoted, thus enhancing interregional connectivity and synergies, improving the overall competitiveness of the construction industry, and ultimately realizing the sustainable growth and prosperity of the regional economy [57].

6. Conclusions

This work identified a key gap in the efficiency of the construction industry in Saudi Arabia; using data from 13 cities in the Saudi Arabian region for the period 2013–2022 as a research sample, it quantified the efficiency of the construction industry in Saudi Arabia based on the super-efficient EBM–Malmquist model and analyzed the factors influencing the efficiency of the construction industry, as well as the spatial correlations to identify the temporal and spatial dimensions of the current status of the construction industry’s development in Saudi Arabia. The findings of this paper are as follows:

• At the level of static efficiency, the technical, purely technical, and scale efficiencies of the construction industry in Saudi Arabia have improved significantly; however, the industry remains at a low level of development. Continuous technological innovation and expansion of the industry’s scale contribute to the improvement in the efficiency of the construction industry.
• In terms of dynamic efficiency, the TFP is generally growing, despite fluctuating changes. This growth is mainly due to EC enhancement. Despite the fact that technological change has slowed in recent years due to the increased cost of technological advancement, Saudi Arabia should accelerate technological development by upgrading international technological exchanges and formulating policies that favor technological innovation in order to improve the competitiveness of the construction industry.
• The results of the regression analysis indicate that population and GDP have the most significant impact on the efficiency of the construction industry in Saudi Arabia. The regression coefficients for these variables are −1.287178 and 1.042445, respectively, ranking first and second. The third most influential factor is carbon dioxide emissions, with a regression coefficient of −0.926266. Although the new construction area passed the significance test, its regression coefficient was only −0.0364423, indicating that the new construction area has a small impact on the efficiency of the Saudi Arabian construction industry. The rapid population growth in Saudi Arabia has resulted in an oversaturated labor market and intensified competition for land resources, which has constrained the efficient development of the construction industry. In contrast, GDP growth improves economic conditions and market demand, which promotes the efficiency of the construction industry. It is worth noting that there is a significant correlation between reductions in carbon dioxide (CO₂) emissions and improvements in the efficiency of the construction industry, which further emphasizes the importance of improving the efficiency of the construction industry to achieve environmental sustainability.
• From the analysis of the spatial autocorrelation, the results of the global Moran’s I calculations indicate that, although there is a certain spatial spillover effect, this effect is relatively weak and does not show a clear trend over the sampling period. This implies the limitations of inter-regional clustering and interactions, thus emphasizing the need to strengthen the cooperation and exchange of regional construction in the construction industry to promote resource sharing and technology diffusion.

Although this study provides a more comprehensive analysis of the current situation of the development of the construction industry in Saudi Arabia, which can provide a certain theoretical basis for the sustainable development of the industry, there are still limitations in the data collection and policy analysis of the impact on the efficiency of the industry, which need to be further improved in future studies. Firstly, the study sampled a limited time span and did not adequately reflect long-term trends and cyclical fluctuations. Second, due to the significant differences in the level of economic development and construction industry policies across regions in Saudi Arabia, there is a need to delve more deeply into the inter-regional variability and the impact of regional policies on the efficiency of the construction industry. Future researchers should expand the size of the sample data and analyze in depth the differences in construction industry policies in different regions to provide more favorable support for the development of the construction industry in Saudi Arabia.