Carbon emission efficiency of China’s construction industry based on super-efficiency SBM and Tobit model

Abstract

Global warming is one of the key issues attracting international concern. The carbon dioxide emission produced by energy combustion is the main cause of the greenhouse effect, and reducing carbon emissions is considered the most effective way to deal with the greenhouse effect. The extensive production mode characterized by high energy consumption, high emission, and low efficiency in China’s construction industry intensifies the contradiction between economic development and resources and the environment, and the growth under this mode is at the expense of consuming a lot of resources and energy. The improvement of carbon emission efficiency is an effective means of achieving the goal of economic growth and carbon emission reduction simultaneously, making it necessary to accurately measure the carbon emission efficiency of the construction industry in each province, determine the influencing factors, and formulate reasonable emission reduction policies for this industry. In this study, an input-output index system of carbon emission efficiency of China’s construction industry was constructed, the carbon emission efficiency of the construction industry in each province was evaluated using the super-efficiency SBM model, and the factors affecting the carbon emission efficiency of this industry were analyzed via the Tobit model. The results showed that the average value of carbon emission efficiency of the construction industry generally showed a rising trend in a fluctuating way during the study period. From 2014 to 2022, the average carbon emission efficiency of the national construction industry presented an upward trend, from 1.122 in 2014 to 1.148 in 2022; the regional economic level ( $p=$ 0.020 $<$ 0.05) and human capital level ( $p=$ 0.000 $<$ 0.01) exerted obvious promoting effects on the carbon emission efficiency of China’s construction industry, while the urbanization development ( $p=$ 0.049 $<$ 0.05) generated evident negative effects on carbon emission efficiency of this industry. The research results have important reference values for making cross-provincial emission reduction plans for the construction industry, promoting its carbon emission efficiency, and driving the research and development of green building materials and clean energy.

Keywords

Super-efficiency SBM Tobit model China’s construction industry carbon emission efficiency energy consumption

1. Introduction

The increasing dependence accompanies the rapid development of human society on energy and the enormous consumption of such fossil energy resources as petroleum, coal, and natural gas. Consequently, global carbon emissions increase sharply, triggering increasingly significant global warming and increasingly severe eco-environmental problems, which have severely restricted the sustainable development of human society. The greenhouse effect leads to an abnormal temperature environment, which greatly threatens our living environment. Various countries have gradually recognized that an essential path to stabilising global temperature and solving climate problems lies in realizing global emission reduction, low-carbon, and sustainable development. Low-carbon development is the primary path to achieving the strategic carbon emission reduction goals in China while controlling the total energy demand under the restriction of low-carbon development and realizing the transformation of each industry constitutes the urgent need for healthy social development. As stipulated by the United Nations Framework Convention on Climate Change in 1992, developed countries must take emission reduction measures to provide financial support to solve the problem of rising global temperatures – the Copenhagen Accord in 2009 reaffirmed governments’ desire to oppose climate change. In 2019, the United Nations Environment Programme (UNEP) published the Emissions Gap Report 2019, requiring countries to make greater contributions to reducing carbon emissions. Despite the economic development to a certain stage, China faces the dilemma of high energy consumption and carbon emissions.

As one of the important industrial sectors of China’s national economy, the construction industry is characterized by strong industrial correlation, high energy consumption, and low resource utilization rate and bears great pressure of emission reduction. China’s construction industry has developed very rapidly in the last decade. The number of construction enterprises and the total output value of the construction industry have both increased rapidly, with an average annual growth rate of 9.11% and 10.50%, respectively. Meanwhile, the construction industry has absorbed many material products from other industrial sectors, which has a significant radiation-driven effect on related industries, especially metallurgy, construction materials, petrochemistry, electromechanics, light industry, etc. The construction industry undertakes a great responsibility for achieving emission reduction targets. Improving the carbon emission efficiency of the construction industry will effectively promote the realization of emission reduction targets. The energy consumption of China’s construction industry accounts for more than 30% of the total social energy consumption, and the construction industry has become the fastest-growing industry in terminal energy consumption and carbon emissions. As a pillar industry of the national economy, the construction industry is always subjected to such problems as high energy consumption, high pollution, and low energy efficiency. The contradiction between the harsh natural environment, resource constraints, and the construction industry’s growing demand is prominent. Improving the carbon emission efficiency of the construction industry is of certain theoretical significance and practical application value for properly resolving this contradiction. Ensuring the steady growth of the construction industry’s output value is an important premise for its emission reduction work; that is, it is expected to maximize the output value in building production activities, minimize carbon dioxide emissions, and improve the carbon emission efficiency of the construction industry, all of which are in line with the great significance. Therefore, improving carbon emission efficiency should be taken as an essential means of reducing carbon emissions and promoting the sustainable development of the construction industry. By measuring the current carbon emissions of the construction industry in China, carbon emission efficiency can be investigated, helping the construction industry save energy and reduce emissions and facilitating China’s achievement of the overall emission reduction targets. In addition, accurately measuring the carbon emission efficiency of the construction industry in each province will help each province formulate emission reduction policies separately and avoid the inapplicability of emission reduction policies caused by ignoring the different regional economic conditions and industrial development levels.

2. Theoretical background

The threat of global warming to human beings speaks for itself, and the development of a low-carbon economy has become the general consensus of governments worldwide. With increasing attention paid to carbon emission reduction, carbon emission efficiency has also become a research hotspot. The research on carbon emission efficiency of the construction industry is mostly based on the perspective of total factors, and data envelopment analysis (DEA) is a dominant calculation method. Relatively abundant factors influencing the carbon emission efficiency of the construction industry have been selected. Using the data from 2005–2016, for instance, Du et al. [1] estimated the carbon emission efficiency of the construction industry in 30 provinces and discussed the spatial distribution characteristics of carbon emission efficiency. The results show that the carbon emission efficiency of China’s construction industry presents unbalanced regional distribution characteristics, i.e., high in the east and low in the west, and a significant spatial spillover effect is observed in carbon emission efficiency. Sizirici et al. [2] concluded that carbon footprints could be reduced by using alternative additives in construction materials, improving the design, recycling construction waste, promoting the utilization of alternative water resources, and improving the efficiency of water technology and other building systems at different construction stages.

Zhou et al. [3] results pointed out that the carbon emission efficiency of China’s construction industry is low, showing a downward trend. Technological progress and energy structure adjustment can promote the carbon emission efficiency of the construction industry, and a common but differentiated carbon emission reduction mechanism should be established among industries. The results of DU et al. [4] showed that the carbon emission efficiency of the construction industry is quite different, and the research results can provide a reference for formulating regional carbon emission reduction policies for the construction industry in China. Liang et al. [5] found that per capita GDP, energy consumption structure, industrial development degree, and industrial concentration positively affect the improvement of energy efficiency in the construction industry. Wu et al. [6] used the logarithmic mean divisor index to evaluate the carbon emissions of the construction industry from the perspective of the whole life cycle of mining, manufacturing, construction, and construction-related transportation and construction operations. It was found that the extraction and manufacturing of raw materials and construction operations are the two greatest contributors to the life cycle carbon emissions of the construction industry.

Liao and Li [7] held that the development of green buildings plays an important role in improving the efficiency of carbon emission reduction in the construction industry and accelerating the construction industry to achieve carbon neutrality. Using the mixed Logarithmic Mean divis index (LMDI) model, Li et al. [8] discussed the driving factors of the total carbon emissions of the inter-provincial construction industry. The results showed that the driving factors of carbon emission in the construction industry in Jiangsu province are the area factor and output value intensity factor, and the inhibiting factor is carbon emission intensity. Zhang et al. [9] proposed an improved DEA method to analyze the carbon efficiency decomposition of the regional construction industry. The carbon efficiency of construction materials in most areas of China is low, which is attributed to the low efficiency of material consumption. The carbon decomposition efficiency of each region shows a decreasing trend from the east to the west, and the regional differences gradually expand. Taking China’s construction industry as the research object, Wu et al. [10] investigated the decoupling relationship between economic output and carbon emissions. The results revealed an expansive decoupling relationship between economic growth and building carbon emissions in most provinces of China. Chen et al. [11] analyzed the carbon efficiency of China’s construction industry and its convergence characteristics among provinces. The results showed that the carbon efficiency of the construction industry is generally stable and fluctuates during the sampling period. The research results of Li et al. [12] showed that the carbon emission efficiency of China’s construction industry was at a low level and increased from 2004 to 2017, indicating an enormous potential for CO₂ emission reduction and the increase of carbon emission efficiency of the construction industry is mainly attributed to technological progress. Lu et al. [13] found that “consumption of construction materials” contributes the most to the increment of carbon emissions, while “energy intensity” offsets the increment of carbon emissions the most. The results of Wang et al. [14] reflected that the regional differences in carbon emissions in the construction industry are significant, and the intensity of carbon emissions in the construction industry shows a fluctuating trend.

Through the empirical analysis of the American housing industry, Lu et al. [15] explained its impact on industrial production, emission reduction targets, market structure, technology selection, and carbon cost allocation. The results of Jiang et al. [16] showed that the optimization of the energy consumption structure of China’s construction industry from 2007–2017 had a significant reduction effect on the growth of energy carbon emissions in China’s construction industry and the energy intensity effect and input structure effect have a positive inhibition effect on the growth of carbon emissions in the construction industry. It can be seen from the existing research that the carbon emission efficiency in the construction industry is calculated mostly based on the framework of total factors, and DEA is the most widely used calculation method. Based on the maximum output setting of the DEA model, the reciprocal of carbon emissions is taken, or the consumption of its alternative energy sources is incorporated into the input index framework, and the carbon emission efficiency calculated by both methods is not accurate enough.

In some papers regarding the SBM model-based calculation for the carbon emission efficiency of the construction industry, the unexpected output has not been fully considered, e.g., the single choice of construction material consumption. In addition, the factors influencing the carbon emission efficiency of the construction industry are mostly focused on the technical level, energy consumption structure, and marketization level. Given the above analysis, an input index-output index system was established in this study to calculate the carbon emission efficiency of the construction industry. Then, the carbon emission efficiency of the construction industry in 30 provinces of China during 2014–2022 was estimated using the super-efficiency SBM model. Afterwards, the factors influencing the carbon emission efficiency of the construction industry in each province were calculated through the Tobit model. By comparing the difference between the current situation of carbon emission efficiency of China’s construction industry and the carbon emission efficiency of sample units and analyzing the causes of the current carbon emission efficiency status in the construction industry of each province, this study provides a more accurate and reasonable basis for making emission reduction policies for China’s construction industry and proposing more pertinent emission reduction strategies for this industry.

3. Methodology

3.1 Modeling

In the super-efficiency SBM-Undesirable model, $n$ mutually independent regional provincial-level construction industries are assumed, each of which has $m$ inputs $x\in R^{m}$ , $k$ expected outputs $y\in R^{k}$ , and one unexpected output $b\in R^{l}$ . Matrices $X=[{x_{1},\ldots,x_{n}}]\in R^{m\times n}$ , $Y=[{y_{1},\ldots,y_{n}}]\in R^{k\times n}$ , and $B=[{b_{1},\ldots,b_{n}}]\in R^{l\times n}$ are defined, and $X>0,Y>0,B>0$ . Planting households pursue the minimum input $X$ , the maximum expected output $Y$ , and the minimum pollution $B$ , in which the production possibility set $P$ is as below:

$\displaystyle P=\{(x,y,b)|x\gg X\lambda,y\ll Y\lambda,b\gg B\lambda,\lambda\gg 0\}$ (1)

Where $\lambda\in R^{n}$ is a weight vector, $\lambda\gg 0$ indicates constant returns to scale, $\lambda\gg 0$ , and $\sum\lambda=1$ means variable returns to scale. $x\gg X\lambda$ reflects that the actual input is greater than the optimal input, $y\ll Y\lambda$ means that the actual expected output is smaller than the optimal expected output, and $b\gg B\lambda$ indicates that the actual unexpected output is greater than the optimal unexpected output. $\delta^{\ast}({0<\delta^{\ast}\ll 1})$ is defined to estimate the technology efficiency of the production environment for the regional provincial-level construction industry under the input-output state of ( ${x_{0},y_{0},b_{0}}$ ). $\delta^{\ast}=1$ indicates efficient environmental technology in this regional provincial-level construction industry. $s_{i}^{-},s_{r}^{+},s_{v}^{-}$ respectively represent the redundant input, insufficient expected output, and excessive unexpected output, which points out the practical direction for further improving the environmental technology efficiency of the regional provincial-level construction industry. It can be known from Eq. (3.1) that $\delta^{\ast}$ is not affected by the data measurement unit, and the input and output slack variables decrease monotonically.

$\displaystyle\delta^{\ast}=\min\delta=\frac{1-\frac{1}{m}\left.\sum\limits_{i=% 1}^{m}s_{i}^{-}\right/{x_{io}}}{1+\frac{1}{k+1}\left(\left.\sum\limits_{r=1}^{% k}s_{r}^{+}\right/{y_{ro}}+\left.\sum\limits_{v=1}^{l}s_{v}^{-}\right/{b_{ro}}% \right)}$ $\displaystyle\textit{s.t. }x_{io}=X\lambda+s_{i}^{-},\forall m;$ $\displaystyle y_{ro}=Y\lambda-s_{r}^{+},\forall k;$ $\displaystyle b_{vo}=B\lambda+s_{v}^{-},\forall l;$ $\displaystyle\lambda_{j}\gg 0,\forall n;s_{i}^{-}\gg 0,\forall m;s_{r}^{+}\gg 0% ,\forall k;s_{v}^{-}\gg 0,\forall l.$ (2)

In actual production, there are usually multiple regional provincial-level construction industries with efficient environmental technology, which cannot be further distinguished due to the setting of the SBM model. To solve this problem, Tone (2001) combined the super-efficiency DEA model with the SBM model and put forward a super-efficiency SBM model. On this basis, the super-efficiency SBM-Undesirable model can be derived to distinguish planting households with efficient environmental technologies. In Eq. (3.1), $w_{i}^{-},w_{r}^{+},w_{v}^{-}$ stand for the minimum input saving, the minimum expected output surplus, and the minimum unexpected output redundancy, respectively.

$\displaystyle\delta^{\ast}=\min\delta=\frac{1+\frac{1}{m}\left.\sum\limits_{i=% 1}^{m}w_{i}^{-}\right/{x_{io}}}{1-\frac{1}{k+1}\left(\left.\sum\limits_{r=1}^{% k}w_{r}^{+}\right/{y_{ro}}+\left.\sum\limits_{v=1}^{l}w_{v}^{-}\right/{b_{ro}}% \right)}$ $\displaystyle\textit{s.t. }x_{io}\gg X\lambda-w_{i}^{-},\forall m;$ $\displaystyle y_{ro}\ll Y\lambda+w_{r}^{+},\forall k;$ $\displaystyle b_{vo}\gg B\lambda-w_{v}^{-},\forall l;$ $\displaystyle\lambda_{j}\gg 0,\forall n;w_{i}^{-}\gg 0,\forall m;w_{r}^{+}\gg 0% ,\forall k;w_{v}^{-}\gg 0,\forall l$ (3)

In the Tobit model, because the environmental technology efficiency values of regional provincial-level construction industries are all greater than 0, a model with limited dependent variables (Tobit) was adopted in this study, expressed as follows:

$\displaystyle E_{i}^{\ast}=\beta_{0}+\beta_{i}X_{i}+\varepsilon_{i,}% \varepsilon_{i}\sim N({0,\sigma^{2}})$ (4) $\displaystyle E_{i}=\max({0,E_{i}^{\ast}})$ (5)

Where $E_{i}$ represents the technology efficiency of the production environment for the $i$ -th regional provincial-level construction industry, $E_{i}^{\ast}$ is an observed value in the interval of ( ${0,+\infty}$ ), $X_{i}$ denotes a series of regional construction industries’ features influencing the technology efficiency of the production environment, $\beta_{i}$ is a parameter vector, and $\varepsilon_{i}$ is an independent random disturbance term. Furthermore, the marginal effect of the estimation coefficient was solved to facilitate the interpretation of regression results.

3.2 Design of evaluation indexes

With reference to the existing research results of scholars, the capital stock, labour force, and energy consumption were selected as the input factors first. Therefore, this study selected the input and output indexes of construction industries’ carbon emission efficiency (Table 1).

Table 1
Input and output indexes of construction industries’ carbon emission efficiency

Index type	Index selection	Index source
Input index	Employment of urban units in the construction industry (ten thousand people)	China Statistical Yearbook on Construction Industry
	Total assets of urban units in the construction industry (one hundred million CNY)	China Statistical Yearbook on Construction Industry
	Number of construction enterprises	China Statistical Yearbook
Output index	Housing construction area of the construction industry (ten thousand square meters)	China Statistical Yearbook
	Total output value of the construction industry (one hundred million CNY)	China Statistical Yearbook
	Added value of the construction industry (one hundred million CNY)	China Statistical Yearbook
Unexpected output index	Carbon emissions of the construction industry (ten thousand tons)	China Energy Statistical Yearbook

In this study, the carbon emissions of the construction industry were mainly selected from the data of 30 provinces (municipalities directly under the central government and autonomous regions, excluding Tibet, Hong Kong, Macao, and Taiwan) in China during 2014–2022, and the direct carbon emissions of the construction industry were calculated respectively. The direct carbon emission of the construction industry mainly came from the energy consumption in the building production process. According to the fuel classification and previous research, 11 kinds of energy sources such as raw coal, coal briquette, coke, gasoline, kerosene, diesel, fuel oil, liquefied petroleum gas, natural gas, heat, and electricity were mainly selected, and the data were collected from the China Energy Statistics Yearbook.

In the Tobit model, the factors influencing the carbon emission efficiency of the construction industry should be selected mainly from external and internal environments. The regional economic development level has been considered a lot in the external environment, while in the internal environment, the focus is on the energy consumption structure and technology level. In this study, four indexes, namely, regional economic level, human capital level, environmental regulation, and urbanization level, were finally selected as the explanatory variables mainly out of the following considerations: (1) Economic development level: adjusted as a constant price with 2014 as the base period according to the per capita GDP. The regional economic development level can be used as a judgment of regional strength, which can not only bring more resources to the construction industry and affect its development but also affect the regional scientific and technological level and thus influence its carbon emission efficiency. (2) The level of human capital: expressed by the regional population’s per capita years of education. The research shows that human capital can improve economic efficiency and reduce carbon emissions by improving enterprises’ factor allocation ability, technological innovation ability, and the ability to absorb external clean technologies. The level of regional human capital is bound to impact the professional technology of construction enterprises, upstream and downstream industries, and auxiliary industries. Meanwhile, the low-carbon concept can be more easily propagated in case of a high level of human capital, which is conducive to the emission reduction of the construction industry and the improvement of carbon emission efficiency. (3) The degree of environmental regulation: expressed by the ratio of government environmental protection expenditure to regional GDP. The degree of regional environmental regulation is measured from the perspective of cost input. The existing studies have shown that environmental regulation has the function of reversely forcing technological innovation and can effectively promote the innovation of construction enterprises in energy conservation, pollution control, emission reduction, and production technologies, which is helpful in improving the carbon emission efficiency of the construction industry. (4) Degree of urbanization: expressed by the proportion of urban population in the total population. The improvement of urbanization will promote the development of the construction industry, increase the demand for building products, consume a lot of construction materials, and increase carbon emissions. However, some studies have shown that urbanization can promote technology agglomeration and industrial structure optimization and has a positive impact on carbon emission reduction in the construction industry.

4. Super-efficiency SBM results

On a nationwide scale, the average carbon emission efficiency of the construction industry showed a rising trend in a fluctuating way during the research period. From 2014 to 2022, the average carbon emission efficiency of China’s construction industry rose from 1.122 in 2014 to 1.148 in 2022. In 2020 and 2021, the carbon emission efficiency of the construction industry was improved, reaching the peak efficiency of 1.148 in 2022 during the research period. 2016–2019 witnessed a sustained reduction of the average carbon emission efficiency in the nationwide construction industry, and the carbon emission efficiency declined to 0.976 in 2019 (in Table 2). This may be because the nationwide urbanization progress has been continuously accelerated in recent years, accompanied by increasing building production activities, which has brought about massive carbon dioxide emissions, but in the current stage, the improvement degree of the green production technology level in the construction industry has not caught up with the urbanization rate and the technology efficiency is low, leading to the decline of the carbon emission efficiency. The carbon emission efficiency of the nationwide construction industry still has a large improvement space, and efforts should be made to continuously promote the carbon emission reduction of regional construction industries and boost their green development.

Table 2
Results of super-efficiency SBM

Province time	2015	2016	2017	2018	2019	2020	2021	2022	Average
Beijing	1.352	0.811	1.165	1.405	0.845	0.930	1.077	1.282	1.108
Tianjin	1.073	1.030	1.117	0.867	0.866	1.109	1.124	1.009	1.024
Hebei	0.888	0.953	0.973	1.316	0.970	1.068	0.999	1.097	1.033
Shanxi	1.052	0.994	1.099	1.104	0.987	0.977	1.049	1.244	1.063
Inner Mongolia	0.847	1.086	0.990	1.097	1.063	1.186	1.137	1.225	1.079
Liaoning	0.616	1.297	0.748	1.064	1.121	1.406	0.992	1.117	1.045
Jilin	0.863	1.058	0.794	1.027	1.065	1.283	1.214	1.060	1.046
Heilongjiang	0.652	1.227	0.859	0.819	0.949	1.269	1.039	1.032	0.981
Shanghai	1.648	0.650	1.261	1.607	0.855	0.904	1.065	1.294	1.161
Jiangsu	1.494	0.568	1.218	1.369	0.846	1.058	0.940	1.374	1.108
Zhejiang	1.224	0.911	1.069	1.002	0.999	1.002	0.868	0.833	0.989
Anhui	1.103	0.864	1.019	1.026	0.882	1.064	0.910	1.077	0.993
Fujian	1.376	0.739	1.031	1.075	0.883	1.349	0.854	1.208	1.064
Jiangxi	1.490	0.694	0.997	0.960	1.181	0.846	0.924	1.017	1.014
Shandong	1.161	0.914	0.936	1.127	1.040	1.025	1.095	1.098	1.050
Henan	1.218	0.895	1.000	1.108	0.881	1.295	0.866	1.110	1.047
Hubei	1.689	0.568	1.062	1.066	1.043	0.986	0.987	1.448	1.106
Hunan	1.232	0.860	1.043	1.149	0.973	0.847	1.008	1.201	1.039
Guangdong	1.229	0.775	2.338	0.578	0.896	1.052	1.612	0.829	1.164
Guangxi	1.352	0.795	1.065	1.201	0.909	1.174	0.794	1.052	1.043
Hainan	1.081	1.000	0.963	1.060	0.994	0.995	0.974	1.017	1.011
Chongqing	1.019	1.073	1.099	1.063	1.030	1.029	0.993	1.392	1.087
Sichuan	1.042	0.951	0.961	1.069	0.962	1.103	0.950	1.044	1.010
Guizhou	1.374	0.607	1.052	0.887	0.956	0.976	0.920	1.079	0.981
Yunnan	0.978	0.957	1.000	0.993	1.045	1.525	1.089	1.337	1.115
Shaanxi	1.069	0.847	1.119	1.129	0.995	0.867	1.028	1.227	1.035
Gansu	0.986	0.870	1.019	0.876	0.960	0.948	0.965	1.062	0.961
Qinghai	0.924	1.168	0.906	1.052	1.015	1.203	1.131	1.284	1.085
Ningxia	0.787	1.167	0.904	0.968	1.196	1.338	1.088	1.178	1.078
Xinjiang	0.840	0.887	0.939	0.995	0.877	1.230	1.051	1.201	1.002

The change trend of carbon emission efficiency of the construction industry in the eastern region was not the same as that of the national average, showing a fluctuating downward state. From 2014 to 2022, the carbon emission efficiency of the construction industry in the eastern region fluctuated greatly, from 1.195 in 2015 to 0.938 in 2019, which was mainly attributed to the decline in the carbon emission efficiency of the construction industry in Tianjin, Guangdong, Shandong, Fujian, and other provinces, of which the carbon emission efficiency in Zhejiang Province, with the most obvious decline, was only 0.837 in 2022, which led to the decline in the carbon emission efficiency of the construction industry in the eastern region; Since 2020, the carbon emission efficiency in the eastern region was improved. The eastern region’s construction industry benefited from economic development, with relatively advanced machinery, equipment, and production technology, higher overall maturity of the construction industry, and a stronger willingness to clean production. Meanwhile, the energy consumption of the construction industry in the eastern region accounted for a relatively large proportion of clean energy consumption, such as electricity. Compared with the construction industry in other regions that relied heavily on high-emission fossil energy, such as coal, some carbon emissions were reduced, so the carbon emission efficiency was relatively high.

The changing trend of carbon emission efficiency of the construction industry in the eastern region was not the same as that of the national average, showing a fluctuating downward state. In 2014, it was 1.149 and then dropped to 1.46 in 2022, with a relatively small decline. This is mainly because Heilongjiang Province and Anhui Province did not reach the efficiency frontier during the research period, with gaps from the eastern region. Hence, the central region should enhance the exchange with the eastern region, improve the market openness degree of the construction industry, and form effective market competition to promote the technological progress of construction enterprises. Moreover, the energy conservation and emission reduction technologies of the construction industry in the eastern region should be learned to improve the carbon emission efficiency in the central region.

From 2014 to 2022, the average carbon emission efficiency in the western region showed a fluctuating upward trend. The change trend was similar to the national average, showing a fluctuating upward state. In 2014, the efficiency value was 1.002, increasing to 1.200 in 2022, which was also the highest level of carbon emission efficiency of the construction industry in the central region during the research period. Due to the low talent advantage in the Western region, the production technology, scientific and technological level, and development maturity of the construction industry were relatively backward, the production factors were not rationally allocated, and the carbon emission efficiency of the construction industry was low. During the research period, the carbon emission efficiency of the construction industry in the western region was always at the lowest level among the three regions, which lowered the overall efficiency level of the country. Therefore, efforts should be made to enhance support for the Western region’s construction industry, improve its maturity, spread the low-carbon concept, and boost its development.

5. Tobit results

It could be seen from Table 3 that the $p$ -value was smaller than 0.05, so the original hypothesis was rejected; namely, the explanatory variables input into this model were effective, and the modelling was meaningful.

Table 3
Likelihood-ratio test of the Tobit model

Variable	-2-fold logarithmic likelihood value	Chi-square value	df	$p$	AIC value	BIC value
Value	5611.524	116.384	4	0.000	5621.524	5639.516

Table 4

Results of Tobit

Item	Regression coefficient		Standard error		$Z$ value	$P$ value	95% CI
Intercept	1272.	515	887.	307	1.434	0.152	$-$ 466.575	$\sim$ 3011.604
Regional economic level	0.	968	0.	415	2.335	0.020	0.156	$\sim$ 1.781
Human capital level	0.	091	0.	026	3.538	0.000	0.040	$\sim$ 0.141
Environmental regulation	2.	094	15.	525	0.135	0.893	$-$ 28.335	$\sim$ 32.522
Urbanization level	$-$ 0.	258	0.	131	$-$ 1.966	0.049	$-$ 0.515	$\sim$ $-$ 0.001
Log (sigma)	8.	973	0.	043	208.509	0.000	8.888	$\sim$ 9.057

We can infer from Table 4 that:

(1)

The regression coefficient of the regional economic level was 0.968, which was significant at the level of 0.05 ( $z=$ 2.335, $p=$ 0.020 $<$ 0.05), meaning that the regional economic level will have a significantly positive impact on the carbon emission efficiency of the construction industry. This reveals that the carbon emission efficiency of the construction industry can be improved by elevating the regional economic level, which resembles the research conclusion of most research documents – in addition, improving the construction industry’s carbon emission efficiency benefits from developing the overall regional economic level, which is also evidenced by the distribution characteristics of the construction industry’s carbon emission efficiency. The carbon emission efficiency is relatively high in the economically developed eastern region. The development of the economic level is beautiful, promoting technological innovation and improving the energy utilization rate. After the economy develops to a certain extent, the government’s and citizens’ environmental awareness is strengthened, which generates a positive effect on reducing carbon emissions in the construction industry.

(2)

The regression coefficient of the human capital level was 0.091, which was significant at the level of 0.01 ( $z=$ 3.538, $p=$ 0.000 $<$ 0.01), reflecting that the human capital level will have a significantly positive impact on the carbon emission efficiency of the construction industry. The main reason is that the improvement of the regional human capital level in different provinces can improve the carbon emission efficiency of the construction industry. There are many employees in the construction industry, and the regional population accounts for a relatively large proportion, so the improvement of the regional overall human capital level will improve the human capital level in the construction industry. Moreover, improving the human capital level in the construction industry will enhance productivity by increasing work proficiency, improving the level of mechanization, and upgrading production technologies, becoming the first factor for economic growth in the construction industry. At the same time, improving the human capital level can promote the carbon emission efficiency of the construction industry by acting on the upstream industries of the construction industry, such as construction materials and architectural design industries. The technological progress brought by the improvement of the human capital water level is not only conducive to the creation of environment-friendly construction materials and the exploration of new energy sources, reducing the dependence on traditional high-carbon construction materials and energy sources but also can optimize building products from the design end and reduce the use of construction materials, thus reducing the carbon emission of the construction industry and improving the carbon emission efficiency.

(3)

The regression coefficient of environmental regulation was 2.094, but it was not significant ( $z=$ 0.135, $p=$ 0.893 $>$ 0.05). This manifests that the enhancement of environmental regulation can improve the carbon emission efficiency of the construction industry, which brings higher requirements for the pollution control of construction enterprises. Despite the increase in capital investments, fiscal and tax subsidies stimulate green technology innovation and enhance the green R&D vitality of construction enterprises. Stimulated by environmental regulation, many green innovation technologies have been hastened to promote green construction and the utilization of environmentally friendly materials, reduce the construction industry’s carbon emissions, and improve its carbon emission efficiency. However, this coefficient did not pass the significance level test of 5%, indicating that the enhanced environmental regulation fails to significantly improve the carbon emission efficiency of the construction industry. This is because, relative to the fiscal and tax subsidies, the decision-makers of some construction enterprises expect to acquire the maximum income from production so that they will increase investments, which leads to the increase of carbon emissions and reduces the carbon emission efficiency.

(4)

The regression coefficient value of urbanization was $-$ 0.258, which was significant at the level of 0.05 ( $z=-$ 1.966, $p=$ 0.049 $<$ 0.05), meaning that urbanization will have a significantly negative impact on the carbon emission efficiency of the construction industry. This reveals that urbanization inhibits the improvement of the carbon emission efficiency of the construction industry and leads to great housing demands and infrastructure construction demands, needing to consume a lot of construction materials and energy resources, which will increase the carbon emissions. However, this coefficient did not pass the significance test. A possible reason is that urbanization increases the carbon emissions of the construction industry while promoting industrial restructuring, accompanied by the elevation of the urban economic level. In addition, the positive effect produced by industrial restructuring will offset the partial negative effect triggered by the increasing carbon emissions.

6. Conclusions

The unbalanced economic development of the construction industry and the congenital differences in natural resources in China lead to obvious regional differences in the construction industry’s carbon emission efficiency, resulting in low carbon emission efficiency and uneven spatial distribution. Based on the super-efficiency SBM model, the carbon emission efficiency of the construction industry in each province was evaluated in this study. While analyzing the carbon emission efficiency of the construction industry in three administrative regions, the influence of each factor on the carbon emission in the construction industry was empirically analyzed through the Tobit model. Finally, the following three conclusions were drawn: (1) During the research period, the average carbon emission efficiency of the construction industry showed a fluctuating upward trend, and the average carbon emission efficiency of the national construction industry showed an upward trend from 2014 to 2022, increasing from 1.122 in 2014 to 1.148 in 2022; (2) an obvious regional imbalance of the carbon emission efficiency was observed in the construction industry among the eastern, central, and western administrative regions of China; (3) the regional economic level and human capital level can promote the carbon emission efficiency of the construction industry in China; urbanization hurts the improvement of the carbon emission efficiency of China’s construction industry. Environmental regulation can promote the efficiency of China’s construction industry’s carbon emission, but it fails to pass the significance level test of 5%. It is suggested that in the future, more accurate index measurement methods should be further selected to investigate interprovincial and intercity differences in the carbon emission efficiency of the construction industry, and the formation mechanism for the differences in the carbon emission efficiency of the construction industry should be deeply explored.

Footnotes

Declarations of interest

None.

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Carbon emission efficiency of China’s construction industry based on super-efficiency SBM and Tobit model

Abstract

Keywords

1. Introduction

2. Theoretical background

3. Methodology

3.1 Modeling

Table 1 Input and output indexes of construction industries’ carbon emission efficiency

Table 2 Results of super-efficiency SBM

Table 3 Likelihood-ratio test of the Tobit model

Footnotes

Declarations of interest

References

Table 1
Input and output indexes of construction industries’ carbon emission efficiency

Table 2
Results of super-efficiency SBM

Table 3
Likelihood-ratio test of the Tobit model