Provincial CO₂ Emission Measurement and Analysis of the Construction Industry under China’s Carbon Neutrality Target

Abstract

The construction industry plays a crucial role in China’s fulfillment of the goal of achieving “carbon neutrality” in 2060. Based on the data of energy and building materials consumption of the construction industry in China and 30 provinces from 2008 to 2018, this paper constructs a model for measuring provincial CO₂ emissions of China’s construction industry and establishes a Kuznets curve and elastic decoupling model of the industry’s CO₂ emissions. The analysis based on the models shows that: (1) the CO₂ emissions of China’s construction industry show a trend of increasing first and then decreasing; (2) in terms of the decoupling effects, most provinces are in a weak decoupling status of CO₂ emissions; and (3) the Kuznets curve of the provincial construction industry shows an inverted “U” shape in recent years, and it is predicted that the CO₂ emissions of the construction industry will reach the peak in 2034. It is possible for the construction industry to achieve “carbon neutrality”, but long-term efforts must be made for strategic planning, policies and regulations, industry standards, etc.

1. Introduction

During the 75th United Nations General Assembly (UNGA), President Xi Jinping promised that China will adopt more effective policies and measures to ensure that China’s carbon emissions will reach the peak by 2030 and that carbon neutrality will be achieved by 2060. The realization of the goal of carbon neutrality is not only a consensus on environmental protection between China and the world, but also a new revolution represented by new energy, which is expected to reshape the rules of world geopolitics. When China gains some experience in promoting “carbon neutrality”, it can also help other countries meet the requirements of the Paris Agreement and contribute to the global response to climate change.

China’s announcement of the goal of carbon neutrality by 2060 is a milestone in the global response to climate changes. By the end of 2019, the CO₂ emissions of Chinese enterprises had decreased by 48.1% compared with 2005. These data show that China has fulfilled the commitment of “reducing the intensity of CO₂ emissions by 40–45% compared with that in 2005 by 2020”, reversing the previous trend of rising greenhouse gas emissions [1]. However, in view of China’s current development stage, the development of many industries and regions is still heavily dependent on fossil fuels, and CO₂ emissions are still rising. According to the measurement of the International Energy Agency, China’s CO₂ emissions increased to a certain extent in 2018 and 2019 [2]. Especially in the first six months of 2020 after the outbreak of the coronavirus pandemic, the CO₂ emissions of heavy industries rose by 25%, including coal and cement plants. As the leading industry of China’s economy, the construction industry accounts for 26.11% of China’s GDP and consumes 2/5 of the world’s concrete and 1/5 of finished steel products, which accounts for 1/4 of the total carbon emissions. The current state of the emissions of the construction industry poses a challenge for China to achieve the goal of “carbon neutrality”.

According to the 2019 Global Status Report for Buildings and Construction issued by the International Energy Agency (IEA) and the United Nations Environment Programme (UNEP), the emissions of the global construction industry increased by 2% from 2017 to 2018, reaching a record high. What is more worrying is that the global population is expected to reach 10 billion by 2060, of which two thirds will live in cities. To accommodate the population of these cities, a new construction area of 230 billion square meters needs to be added, which means to double the existing construction stock. Such a huge construction demand, coupled with the continuous improvement of urbanization, means that the greenhouse gas emissions of the construction industry will continue to rise. Currently, the scale of China’s construction industry ranks first in the world, with a total urban construction stock of about 65 billion square meters. On this basis, China’s annual new construction area is about 2 billion square meters, equivalent to nearly 1/3 of the global total new construction (6.13 billion square meters).

Another challenge comes from the 2019 Refinement to the 2006 IPCC Guidelines for National Greenhouse Gas Inventories released on 12 May 2019 at the 49th plenary meeting of the IPCC in Kyoto, Japan [3]. The refinement has a profound impact on the calculation of air pollutants around the world. In the energy industry, more sophisticated management and more detailed user manuals have a certain impact on China’s pollution emission inventory, e.g., increasing the greenhouse gas emissions within the accounting scope of China. China is the largest producer and consumer of coal and the second largest consumer of oil in the world. The amount of greenhouse gas escape is obvious and cannot be ignored in the important links of energy production, processing, distribution and transportation. Combined with the latest emission coefficient, the task of CO₂ emission reduction in China’s construction industry will become more difficult, and the process of achieving the goal of carbon neutrality will be delayed. The task of energy conservation and emission reduction of the construction industry will also be more challenging.

Therefore, whether China can achieve “carbon neutrality” by 2060 will depend on the performance of the construction industry to a certain extent. Against this background, this paper calculates and analyzes the CO₂ emissions of China’s construction industry. Based on the scale and change trend of the CO₂ emissions of the construction industry in 30 provinces and cities in China, the elastic decoupling model and the Kuznets curve of CO₂ emissions are applied to study the decoupling of CO₂ emissions and the relationship between the CO₂ emissions and the economic development of the construction industry. Based on the study, this paper provides some planning suggestions which may help reach the goal of having the carbon emission peak in 2030 and achieving carbon neutrality in 2060 and may be of certain value for other countries in responding to climate change.

2. Literature Review

As an important strategy of economic and social development, low-carbon economy has been a field of common concern in recent years. With the development of low-carbon theories in recent years, scholars worldwide have conducted some research on carbon emissions of the construction industry. In terms of the calculation scope, Zhang et al. (2013) put forward the definition of relevant carbon emissions and classified the emissions into direct and indirect carbon emissions in the process of carbon emission measurement of China’s construction industry [4]. Chau et al. analyzed the construction materials used in some buildings of HongKong and came to the conclusion that external walls had the highest CO₂ footprint [5]. In terms of calculation methods, Qi et al. used the economic input–output analysis method to calculate the direct and indirect carbon emissions of China’s construction industry from 1995 to 2009 [6]; Mao et al. set a calculation boundary with five emission sources for the semi-prefabricated construction process and then established a quantitative model [7]; Li et al. combined input–output analysis with process analysis to quantify the carbon emissions of an office building in Yizhuang, Beijing [8]; Elena et al. (2021) compared the primary energy consumption and elaborated the importance of embodied energy in Hellenic residential buildings during their life cycle [9]; and Wan demonstrated hybrid life cycle assessment (LCA) to Malaysian building design systems and improved the completeness of inventory data of the embodied energy and carbon [10].

Low-carbon development and environmental protection requires reducing CO₂ emissions, on the one hand, and taking into account the social and economic development, on the other hand. Wang et al. analyzed the internal tension between environmental conservation and degradation in a globalizing world and discussed the opportunities for less developed countries to reduce emissions [11]. Luo et al. quantified direct and indirect emissions associated with off-site construction, and the results indicated that conducting a well-articulated assessment was important to assist with decision making on environmental impact reduction [12]. According to the new economic development model, we should not only control CO₂ emissions but also promote social and economic development. Research should be made into the connection between the two. Previous research on carbon emissions and economic development mainly adopted the Kuznets curve (EKC) and Tapio’s decoupling theory. The Kuznets curve was put forward by the renowned American economist Kuznets. According to the theory, if there is income inequality in a country, there is an inverted U-shaped curve that expands first and then shrinks with economic development. Grossman et al. applied the Kuznets curve to describing the environment between air pollution and economic development [13].

Subsequently, a large number of empirical studies were conducted using the Kuznets curve theory. Kang et al. used the entity model of spatial panel data to study the EKC curve of China and found that China’s rapid economic growth and CO₂ emissions had an inverted N-type correlation [14]. Lin et al. used the environmental Kuznets model to simulate CO₂ emissions and found that there were differences between them, indicating that different industrial structures and energy structures would inevitably lead to different emissions [15]. Davidmac et al. used Pedroni’s cointegration and quantile regression over the 1996–2014 period and found that Africa and its regions are characteristic of a homogenous N-shaped relationship between economic development and environmental degradation [16]. Sefa et al. tested EKC for a panel of eight Australian states over the period 1990 to 2017, and the results show an inverted U-shaped EKC, which peaks in 2010 and declines thereafter [17].

The decoupling theory is a basic model for studying the relationship between economic development and environmental pollution. Guo et al. used the Tapio decoupling entity model to calculate the CO₂ emissions of 30 provinces from 2004 to 2015 and studied the effectiveness of environmental governance policies on reducing carbon emissions [18]. Chao et al. analyzed the trends in transportation CO₂ emissions for 29 Eurasian countries and tested the link between transportation development and CO₂ emissions using the Tapio index [19]. As for the construction industry, Ya et al. examined the decoupling relationships between economic output and carbon emission by focusing on China’s construction industry at both national and provincial levels from 2005 to 2015 [20]. Yan et al. established a Tapio-Z decoupling model and explored the decoupling status of China’s CO₂ emissions at the provincial level and its dynamic path over the period 2000–2016 [21].

Based on the above literature review, it can be found that the research on the carbon emission of the construction industry has been extended to indirect carbon emission and the relationship between carbon emissions and economic growth. However, under the new goal of carbon neutrality, the change in carbon emissions of China’s construction industry still needs more empirical research. This paper uses the IPCC emission index method to calculate the direct emissions of the construction industry based on the energy consumption of construction projects. According to the core concept of the life cycle, the carbon emissions generated by the production of building materials are regarded as indirect carbon emissions, which are included in the calculation model of carbon emissions of the construction industry. In addition, the EKC curve and Tapio decoupling model are established to study the relationship between the CO₂ emissions and economic development of the construction industry.

3. Methodology and Models

3.1. CO₂ Emission Measurement of the Construction Industry

3.1.1. Determination of CO₂ Emission Boundary of the Construction Industry

In carbon-related research, “carbon emission” and “carbon dioxide emission” are two different concepts. Carbon emission is a general term for greenhouse gas emissions, including the greenhouse gas emissions of a region, a group or an organism, measured by carbon dioxide equivalent. However, carbon dioxide emissions are a part of carbon emissions, which is the definition adopted in this paper. The carbon dioxide emission of the construction industry mentioned in this paper refers to the carbon dioxide gas emitted by the construction industry.

To discuss when the CO₂ emissions of the construction industry will reach the peak, the first step is to define the scope of the CO₂ emissions of the construction industry. The CO₂ emissions of buildings under the IPCC system generally include only direct CO₂ emissions. In this paper, the emission coefficient method was used to calculate the total CO₂ emissions of China and of the construction industry. According to the correlation of CO₂ emissions in the industry and the concept of the construction project life cycle, the CO₂ emissions of the construction industry are divided into direct emissions and indirect emissions. Direct CO₂ emissions of the construction industry refer to the amount of CO₂ emissions caused by the construction industry’s inherent activities, that is, CO₂ emissions caused during the design of building planning, engineering construction, demolition processes, etc. This paper analyzes the energy consumption of direct emission pollutants in the construction industry, which is consistent with the analysis of the China Energy Statistical Yearbooks. The direct energy consumption includes raw coal, briquette, coke, other coal washings, other coking products, gasoline, kerosene, diesel oil, fuel oil, lubricating oil and solvent oil. Indirect CO₂ emissions of the construction industry refer to the CO₂ emissions caused by other industries related to the construction industry. For example, in the study of Feng et al. [22], according to the concept of the whole life cycle of construction, the CO₂ emissions caused by five types of building materials (cement, steel, glass, wood and aluminum) produced by relevant industries are used as indirect emissions of the construction industry. After 2012, the China Energy Statistical Yearbooks did not classify secondary energy such as heat and electricity as energy consumption of the construction industry. Considering the continuity and unity of data, the CO₂ emission sources of the construction industry in each province are defined as direct CO₂ emissions from 11 primary energy sources (raw coal, briquette, coke, other coal washings, other coking products, gasoline, kerosene, diesel, fuel oil, lubricating oil and solvent oil) and indirect CO₂ emissions from five types of building materials (cement, steel, glass, wood and aluminum).

3.1.2. Calculation of Direct CO₂ Emissions of the Construction Industry

This paper adopted the emission coefficient method to calculate direct CO₂ emissions, that is, the energy consumption in construction activities multiplied by the respective CO₂ emission factors. Therefore, the basic calculation method of direct CO₂ emissions of the construction industry is as follows:

$$DC={\displaystyle \sum }_{i=1}^{11}{E}_i{a}_i \left(i=1,2,3, \dots , 11\right)$$

(1)

In Equation (1),$E_i$ is the consumption of the i-th energy by the construction industry from the 2009–2019 China Energy Statistical Yearbooks; and $a_i$ is the carbon emission index of the i-th energy source. In other words, the calculation method of the carbon emission index multiplies the product of the combustion calorific value of energy materials, the carbon content in unit heat and the oxidation factor of combustion carbon. For the information on the carbon emissions of the escape link, the emission factors in the 2019 Refinement should be followed. The carbon emission index of different energy sources is shown in

Table 1. Carbon emission coefficient of different energy sources.

Category	Carbon Emission Coefficient	Category	Carbon Emission Coefficient
Raw coal	2.69 kg/kg	Gasoline	3.064 kg/kg
Briquette	2.663 kg/kg	Kerosene	3.147 kg/kg
Coke	3.14 kg/kg	Diesel oil	3.179 kg/kg
Other coal washings	2.436 kg/kg	Fuel oil	3.12 kg/kg
Other coking products	3.15 kg/kg	Lubricating oil	2.95 kg/kg
Solvent oil	3.17 kg/kg	-	-

.

3.1.3. Calculation of Indirect CO₂ Emissions of the Construction Industry

The calculation of indirect CO₂ emissions also uses the emission factor method, which is as follows:

$$IC={\displaystyle \sum }_{j=1}^5{M}_j{\beta }_j(1-{\epsilon }_j) \left(j=1,2,3,\dots ,5\right)$$

(2)

In Equation (2), $M_j$ is the consumption of the j-th building material used by the construction industry. The application data of building materials in the construction industry in each province and region were taken from the statistical tables in the China Construction Statistical Yearbooks from 2008 to 2019.${\beta }_j$ is the carbon emission coefficient of the j-th building material. When calculating the indirect CO₂ emissions of the construction industry, this paper adopted the CO₂ emission indexes of five types of building materials created by Hao [23], Wei [24] and Yan [25], as shown in

Table 2. Carbon emission coefficient of building materials.

Category	Carbon Emission Coefficient
Steel products	2.1219 kg/kg
Wood	−842.8 kg/kg
Cement	0.884 kg/kg
Glass	0.9655 kg/kg
Aluminum	2.6 kg/kg

. In the equation, ${\epsilon }_j$ is the recovery coefficient of the j-th building material. Following the method of previous studies, the recyclability of the building materials is fully considered, and the recycling index of building materials is increased. The value of steel is 0.8, and that of the aluminum profile is 0.85.

3.1.4. Calculation of Total Carbon Emissions of the Construction Industry

The total CO₂ emission amount of the construction industry is equal to the sum of direct and indirect emissions. The basic calculation method of total CO₂ emissions from the construction industry is as follows:

$$TC=DC+IC={\displaystyle \sum }_{i=1}^{11}{E}_i{a}_i+{\displaystyle \sum }_{j=1}^5{M}_j{\beta }_j(1-{\epsilon }_j)$$

(3)

3.2. Decoupling Model Building of the Construction Industry

Decoupling is used to describe whether the relationship between variables with a correlation is continuous. In order to solve the contradiction between economic development and environmental damage, the OECD put forward the concept of decoupling for the first time to describe the relationship between economic growth and carbon dioxide. Decoupling elasticity in this paper refers to the change in CO₂ emissions of the construction industry relative to the annual output value of the construction industry. According to the accuracy and time limit of decoupling, this study adopted the Tapio elastic decoupling entity model and set the decoupling model of the provincial carbon emissions of China’s construction industry as

$${\phi }_{\left(C,CGDP\right)}=\frac{\mathsf{\Delta }C/C}{\mathsf{\Delta }CGDP/CGDP}$$

(4)

where ${\phi }_{\left(C,CGDP\right)}$ refers to the decoupling index between the carbon emission of the construction industry and economic development; C is the carbon emissions of the construction industry; ΔC is the change in carbon emissions in the base period; CGDP is the annual output value of the construction industry; and ΔCGDP is the change in the annual output value of the construction industry in the base period.

In the Tapio model, there are eight decoupling statuses according to the value of decoupling. See

Table 3. Eight decoupling statuses.

Decoupling Statuses		$\mathbf{\Delta }\mathit{C}$	$\mathbf{\Delta }\mathit{C}\mathit{G}\mathit{D}\mathit{P}$	$\mathbf{Elastic} \mathbf{Level} \mathit{\phi }$
Negative decoupling	Strong negative decoupling	>0	<0	$\phi <0$
	Weak negative decoupling	<0	<0	$0<\phi <0.8$
	Expansive negative decoupling	>0	>0	$\phi >1.2$
Decoupling	Strong decoupling	<0	>0	$\phi <0$
	Weak decoupling	>0	>0	$0<\phi <0.8$
	Recessive decoupling	<0	<0	$\phi >1.2$
Connected	Growth connection	>0	>0	$0.8<\phi <1.2$
	Declining connection	<0	<0	$0.8<\phi <1.2$

for details.

3.3. EKC Model Building for the Construction Industry

The environmental Kuznets hypothesis describes the relationship between economic growth and environmental pollution. This paper mainly studies the relationship between the output value of the construction industry and the provincial carbon emissions to build the environmental Kuznets curve (EKC) model. In previous studies, there were four key states in EKC diagrams: “N”, inverted “N”, “U” and inverted “U”. Therefore, we will test the coefficients of the third and second models separately and determine the Akaike information criterion (AIC) and the coefficient of determination R² of the EKC model based on the analysis of the significant differences in the relevant variables in this paper. The model used in this paper is as follows:

$$E_t={\beta }_0+{\beta }_1{Y}_t+{\beta }_2{Y}_t^2+\epsilon$$

(5)

$$E_t={\beta }_0+{\beta }_1{Y}_t+{\beta }_2{Y}_t^2+{\beta }_3{Y}_t^3+\epsilon$$

(6)

Among them, $E_t$ is the CO₂ emission of each province at t; $Y_t$ is the annual output value of the economic development of the construction industry at t; ${\beta }_0$ is a constant term, and ${\beta }_1$ and ${\beta }_2$ are the main parameters; and ε is the random error.

According to the EKC model for statistical analysis of panel data proposed by Shafik and Bandyopdhyay [26], the functions in Equations (5) and (6) are transformed into multiple numbers:

$$\ln {\left(\frac{E}{P}\right)}_{it}={\beta }_0+{\beta }_{1,i}\ln {\left(\frac{Y}{P}\right)}_{it}+{\beta }_{2,i}{(\ln {\left(\frac{Y}{P}\right)}_{it})}^2+{\epsilon }_{it}$$

(7)

$$\ln {\left(\frac{E}{P}\right)}_{it}={\beta }_0+{\beta }_{1,i}\ln {\left(\frac{Y}{P}\right)}_{it}+{\beta }_{2,i}{(\ln {\left(\frac{Y}{P}\right)}_{it})}^2+{\beta }_{3,i}{(\ln {\left(\frac{Y}{P}\right)}_{it})}^3+{\epsilon }_{it}$$

(8)

The positive and negative values of coefficients ${\beta }_1$, ${\beta }_2$ and ${\beta }_3$ are different, which means that the EKC curves are not the same as shown in

Table 4. Environmental Kuznets curve (EKC) curve shape classification.

Category	${\mathit{\beta }}_1$	${\mathit{\beta }}_2$	${\mathit{\beta }}_3$	Curve Shape
1	=0	=0	=0	Not related
2	<0	=0	=0	Monotone decreasing line
3	>0	=0	=0	Monotone increasing line
4	<0	>0	=0	“U” curve
5	>0	<0	=0	Inverted “U” curve
6	>0	<0	>0	“N” curve
7	<0	>0	<0	Inverted “N” curve

.

4. Data and Results

4.1. Provincial Carbon Emissions of the Construction Industry

According to the China Energy Statistical Yearbooks and China Construction Statistical Yearbooks, 30 provinces, municipalities and autonomous regions in China (hereinafter referred to as the 30 provinces) were selected as the research objects. Due to a lack of data, the Tibet Autonomous Region, Hong Kong, Macao and Taiwan were not included. According to the calculation model for CO₂ emissions of the construction industry previously created, the data of the 30 provinces are included in Equation (3) to calculate the CO₂ emissions of the construction industry of the provinces in China since 2008, as shown in

Table 5. Carbon emissions of China’s construction industry in 2008–2018.

Province	2008	2009	2010	2011	2012	2013	2014	2015	2016	2017	2018
China	101,405	113,848	146,782	267,755	344,038	228,438	248,352	187,734	194,826	205,493	220,237
Beijing	2363	3435	4036	4095	3311	3784	3901	3824	3569	3771	4594
Tianjin	1480	2023	1984	2785	2855	3899	5405	3413	3008	2616	4164
Hebei	4100	4290	12,038	54,100	51,409	18,934	7992	8509	5998	6184	4901
Shanxi	3227	3186	4863	3222	3290	3390	4011	3441	3410	3608	3831
Inner Mongolia	3617	2185	2213	2353	1914	1792	1768	1841	2133	2840	1281
Liaoning	3597	5069	6369	10,419	8488	15,831	15,364	5044	4667	2614	2533
Jilin	1180	1563	1546	1691	102,483	7169	7638	2133	1043	919	2136
Heilongjiang	1107	1195	1410	1748	1530	1583	1661	1249	1165	1098	853
Shanghai	2190	2343	2441	2579	2353	2437	2503	2080	2143	2403	2230
Jiangsu	14,515	14,894	17,103	69,605	30,072	23,328	24,842	22,550	21,844	22,093	24,182
Zhejiang	18,647	19,713	22,562	27,194	29,146	32,955	33,886	33,168	33,520	35,754	29,588
Anhui	2812	3288	4525	4776	4700	5894	6362	5340	6738	8665	9829
Fujian	3990	4985	6273	6074	7732	10,445	13722	13,820	16,002	20,280	23,417
Jiangxi	1534	1828	2092	3246	3236	4252	2073	5495	5393	7000	7939
Shandong	5861	7195	8141	7788	19,010	9803	10,157	9188	7908	7901	8313
Henan	4863	5951	7304	7281	8989	8548	23,601	6343	8570	9002	15,013
Hubei	3289	3935	3846	8519	16,409	14,951	17,631	14,381	17,927	14,763	15,707
Hunan	4864	5898	6389	5725	6515	7182	7644	7856	7563	8102	9503
Guangdong	3642	4132	5392	8571	2299	7199	7700	6634	5738	8449	8548
Guangxi	1124	1476	1773	1968	2693	2053	2277	1442	2545	3075	2791
Hainan	177	234	234	394	377	479	305	191	242	275	397
Chongqing	2760	2781	4949	4952	4551	5460	5661	5493	5747	5532	5651
Sichuan	3944	4981	11,029	13,132	20,132	20,932	23,268	9539	9268	13,701	16,158
Guizhou	772	1199	771	1251	1512	2709	3316	4222	7893	3379	4172
Yunnan	1373	1621	2238	1823	2515	5940	6552	2889	3042	3267	3235
Shaanxi	3310	3511	3829	6552	4268	4581	5194	5026	5451	5583	7156
Gansu	1387	920	884	2077	1433	2035	2192	1600	2027	1762	1893
Qinghai	312	439	644	320	342	360	389	371	405	486	478
Ningxia	330	379	458	585	563	768	843	542	531	539	620
Xinjiang	877	880	1085	3659	1830	1718	2375	1750	1557	1553	1256

.

According to the level of economic development and geographical location, the country is divided into five regions: Northeast, East, Central, South and Northwest. Among them, Northeast China includes Heilongjiang, Jilin and Liaoning; East China includes Shanghai, Jiangsu, Zhejiang, Anhui, Fujian, Jiangxi and Shandong; North China includes Beijing, Tianjin, Shanxi, Hebei, Henan, Hubei and Hunan; South China includes Guangdong, Guangxi, Hainan, Sichuan, Guizhou, Yunnan and Chongqing; and Northwest China includes Shaanxi, Gansu, Qinghai, Ningxia, Xinjiang and Mongolia. The average carbon emissions of the construction industry in China and five regions are shown in

Table 6. Average carbon emissions of the construction industry in China and five regions from 2008 to 2018.

Region	2008	2009	2010	2011	2012	2013	2014	2015	2016	2017	2018
China	3380	3795	4893	8925	11,468	7615	8278	6258	6494	6850	7341
Northeast	1961	2609	3108	4619	37,500	8194	8221	2808	2292	1544	1841
East	7078	7749	9019	17,323	13,750	12,731	13,364	13,092	13,364	14,871	15,071
Central	3455	4102	5780	12,247	13,254	8670	10,027	6824	7149	6864	8245
South	1970	2346	3769	4584	4868	6396	7011	4344	4925	5383	5850
Northwest	1639	1386	1519	2591	1725	1876	2127	1855	2017	2127	2114

.

From Table 6, it can be clearly seen that the CO₂ emissions of the construction industry in China’s provinces show a trend of first rising and then decreasing over time. From 2008 to 2012, they maintained a growth trend, and then changed into a downward trend from 2012 to 2018. In 2008 and 2009, the output value of China’s construction industry grew slowly, and carbon emissions also maintained a slow growth. In 2010 and 2011, when China responded with a series of effective policies, China’s economy recovered from the turmoil of the financial crisis, and the carbon emissions of the construction industry also showed a trend of rapid growth. After the 12th Five Year Plan, China increased efforts to adjust the industrial structure, paid more attention to energy conservation and emission reduction and curbed high energy consumption. Through the control of total pollutant emissions in the production of steel, cement, glass and other materials in building materials and the regional spatial layout of the building materials industry, the increase in indirect CO₂ emissions was controlled. Therefore, the total carbon emissions also showed a downward trend potential.

At the same time, it can be seen from Table 6 that the CO₂ emissions of the construction industry in China vary greatly between the regions. Among them, the carbon emission of the construction industry in East China is the highest and that in Northwest China is the lowest, which indicates that the annual output value of the construction industry is positively correlated with the CO₂ emission of the construction industry. In order to reflect the trend analysis of the CO₂ emission of the construction industry in China and the five major regions, Figure 1 is shown below.

Overall, the average carbon emissions increased first and then decreased, and the average CO₂ emissions of the three Northeast provinces increased sharply in 2012. This is because in the data of cement consumption in the region, Jilin Province increased more than 50 times compared with the previous year, significantly increasing the carbon emissions of the three Northeast provinces. According to the previous calculation, indirect emissions are an important source of carbon emissions in the construction industry, accounting for about 95% of the total carbon emissions. Therefore, the environmental protection standards and energy-saving requirements of building materials are more in line with the carbon neutrality goal. The use of hollow energy-saving building glass can significantly reduce building energy consumption; and the use of gypsum board and other lightweight building materials instead of the traditional cement wall and brick wall can also reduce the carbon emissions in the manufacturing process of cement and building bricks and the energy consumption and emission in the transportation process. These are effective attempts by the construction industry to reduce the carbon emissions by reducing the indirect emissions.

4.2. Decoupling Analysis of Inter-Provincial Construction Industry Carbon Emissions

According to the Tapio decoupling model of carbon dioxide emissions of the construction industry and economic growth, C and ΔC are calculated by the carbon emission accounting model, and CGDP and ΔCGDP are obtained from the China Energy Statistical Yearbooks. The relevant data from 30 provinces in China are applied to Formula (4) to calculate the decoupling elasticity between carbon dioxide emissions of the construction industry and economic growth in China and the 30 provinces from 2008 to 2018. The calculation results are shown in

Table 7. Decoupling elasticity of the construction industry in China and the 30 provinces.

Region	Decoupling Index	Decoupling Status	Region	Decoupling Index	Decoupling Status
China	0.475	Weak decoupling	Henan	0.403	Weak decoupling
Beijing	0.395	Weak decoupling	Hubei	0.711	Weak decoupling
Tianjin	0.460	Weak decoupling	Hunan	0.377	Weak decoupling
Hebei	0.146	Weak decoupling	Guangdong	0.696	Weak decoupling
Shanxi	0.127	Weak decoupling	Guangxi	0.603	Weak decoupling
Inner Mongolia	−1.215	Strong decoupling	Hainan	0.391	Weak decoupling
Liaoning	−0.579	Strong decoupling	Chongqing	0.421	Weak decoupling
Jilin	−0.031	Strong decoupling	Sichuan	0.572	Weak decoupling
Heilongjiang	−0.024	Strong decoupling	Guizhou	0.519	Weak decoupling
Shanghai	0.141	Weak decoupling	Yunnan	0.407	Weak decoupling
Jiangsu	0.285	Weak decoupling	Shaanxi	0.406	Weak decoupling
Zhejiang	0.566	Weak decoupling	Gansu	0.226	Weak decoupling
Anhui	0.971	Growth connection	Qinghai	0.539	Weak decoupling
Fujian	1.034	Growth connection	Ningxia	0.471	Weak decoupling
Jiangxi	0.956	Growth connection	Xinjiang	0.363	Weak decoupling
Shandong	0.219	Weak decoupling	-	-	-

.

In this paper, the Tapio decoupling model is used to analyze the decoupling status of CO₂ emissions in the construction industry from 2008 to 2018. In Table 7, the standard values of the whole country and 23 provinces are relatively low, and they are in the state of weak decoupling of CO₂ emissions. This shows that in the selected years, after the global financial crisis and the green development policy of the Chinese government, the economic growth mode of the construction industry in most provinces was in the stage of environmental friendliness. However, Anhui, Fujian and Jiangxi are in the state of growth connection, which means that the development of the construction industry in these provinces is still in the stage of simultaneous growth of environmental quality and economy, is heavily dependent on the consumption of energy and building materials and should be the focus of the government’s emission reduction work. Inner Mongolia and four provinces in Northeast China are in a strong decoupling status, indicating that the environmental quality there has been improved together with economic growth. To sum up, the government can formulate emission reduction targets for all provinces and cities and issue differentiated energy conservation and emission reduction policies for the construction industry, so as to help the construction industry reach the goal of carbon peak and carbon neutrality as soon as possible.

In order to better study the relationship between CO₂ emissions of the construction industry in each province from 2008 to 2018 and analyze the trend during this period, the decoupling coefficient change in China and five regions in 2009–2018 is shown in Figure 2.

From the CO₂ decoupling index of the construction industry in China as a whole and the five major regions, it can be seen that the five regions in China experienced an upward trend from growth connection to expansion negative decoupling from 2009 to 2011, and in 2011, the five regions in China experienced an upward trend of negative decoupling from growth connection to expansion. From 2011 to 2015, the regions changed from expansive negative decoupling to weak decoupling, or from strong decoupling to expansive negative decoupling, and then to weak decoupling and strong decoupling. Generally, it was a trend of first decreasing, then increasing and then decreasing. From 2016 to 2018, most regions maintained weak decoupling and strong decoupling. From the perspective of the five major regions, the Northeast and Northwest regions have differences compared with other regions and even lagged in decoupling, which is also related to the different energy conservation and emission reduction measures and implementation efforts of local governments. Therefore, the Northeast and Northwest regions need to strengthen emission reduction measures and develop new energy sources.

4.3. EKC Analysis of Inter-Provincial Construction Industry Carbon Emissions

The China Energy Statistical Yearbooks and China Construction Statistical Yearbooks contain information about the national and provincial per capita CO₂ emissions, the per capita output value and the inter-provincial CO₂ emissions of the construction industry described in the calculation results. After excluding Jilin Province, which experienced a sharp increase in carbon emissions in 2011, and Hainan Province, which had a large fluctuation in the number of construction workers, the panel data of the whole country and the other 28 provinces were analyzed.

The main purpose of the panel unit root test is to confirm the stability of panel data. This paper adopts the ADF test and IPS test, with the results shown in

Table 8. Panel unit root test results of variables.

Variable	ADF Test	IPS Test	Stability
$\ln C{O}_2$	−12.99 ***	−2.77 ***	Stable
$\mathrm{lnP}C{O}_2$	−5.15 ***	−3 ***	Stable
$\ln PGDP$	−4.48 ***	−3.72 ***	Stable
${(\ln PGDP)}^2$	−5.15 ***	−3.73 ***	Stable
${(\ln PGDP)}^3$	−5.37 ***	−4.72 ***	Stable

Note: ***, ** and * are significant at the levels of 1%, 5% and 10%, respectively.

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It can be seen from Table 8 that the results of the ADF test and IPS test of variable panel data are basically the same. The variables $\ln C{O}_2$, $\mathrm{lnP}C{O}_2$, $\ln PGDP, {(\ln PGDP)}^2$ and ${(\ln PGDP)}^3$ all passed the stationary test, which shows that the data were stable and did not need co-integration detection.

In this paper, the Hausman test is used to verify the application of the fixed effect model or random effect model on the model. The test results are shown in

Table 9. Test results of the fixed effect model and random effect model selection.

Correlated Random Effects—Hausman Test
Test Summary	Chi-Sq. Statistic	Chi-Sq. d.f.	Prob.
Cross-section random	9.728	3	0.002

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Through the Hausman test, it can be seen from Table 9 that the chi-square statistic is 9.728 and the corresponding p value is 0.002, indicating that the test was passed at the level of 1%. Therefore, the original hypothesis was rejected and the fixed effect model was selected for analysis.

Then, using the per capita output value of the construction industry as the independent variable and the per capita CO₂ emission of the construction industry as the dependent variable in the provinces and cities of China from 2008 to 2018, the EKC model was used to fit the model. After that, the significance test of the two regression equations was carried out. Based on the comparison of the coefficient of determination R², F-statistic and t-value, the optimal model was selected for the final regression analysis. The coefficient of determination R² is an important index that reflects the goodness of fit of the regression equation and sample observations, reveals the proportion of independent variables and explains the fluctuation of dependent variables. The F-test is used to test the significance of a regression equation directly according to the regression effect. The t-value test is used to test the validity of a regression coefficient and explain whether the influence of a dependent variable on an independent variable is significant.

The regression results are shown in

Table 10. Model regression results.

Curve	${\mathit{\beta }}_1$	${\mathit{\beta }}_2$	${\mathit{\beta }}_3$	${\mathit{R}}^2$	F-Statistic
Conic	2.39 ***	−0.23 ***	-	0.86	27.9
	(8.330)	(−7.833)	-	-	-
Cubic	5.54 ***	−1.09 ***	0.07 ***	0.81	23.8
	(4.073)	(−4.380)	(4.390)	-	-

Note: ***, ** and * are significant at the levels of 1%, 5% and 10%, respectively. The number in the brackets is the t-value.

.

From the regression results, it can be seen that the coefficients of the two equations are based on the significance test at the significance level of 1%. According to the coefficient of determination R² and F-statistic, the quadratic curve is selected as the best fitting model, and the following regression equation is obtained:

$$\ln {\left(\frac{E}{P}\right)}_{it}=2.39\ln {\left(\frac{Y}{P}\right)}_{it}-0.23{(\ln {\left(\frac{Y}{P}\right)}_{it})}^2+{\epsilon }_{it}$$

(9)

In the regression equation, given the standards${\beta }_1$> 0, ${\beta }_{2 }$< 0 and ${\beta }_3$= 0, it can be indicated that the average CO₂ emissions in the construction industry have an inverted U-shaped relationship with the per capita output value of the construction industry. In the process, the per capita output value of the construction industry is increasing, and the per capita CO₂ emissions of the construction industry first increase and then decrease. There is a turning point in the inverted U-shaped curve at PGDP₀ = CNY 1.80485 million, which can be obtained by calculation. Up to now, the turning point has not come. If the growth rate of the per capita output value of the construction industry is maintained at 9.22% and 9.91%, the inflection point of carbon emissions of China’s construction industry will appear in 2034. At present, China is still on the left side of the inflection point and still needs to go through a period of time to reach the peak. However, there is still a certain space for China to reach the overall carbon peak target in 2030. The construction industry should also actively respond to the call of the state to reduce the CO₂ emissions of the whole industry.

5. Discussion

China’s commitment is to achieve the overall goal of carbon neutrality by 2060, which is far beyond the guarantee target of a 2 °C temperature rise stipulated in the Paris Agreement, so as to complete the global carbon neutrality regulation from 2065 to 2070. This means that China still needs to ensure 30 years of sustained and rapid emission reduction after reaching the peak around 2030, which has a strong transformation meaning for energy, transportation, industry, construction and agriculture and releases an important signal for new investment and the exit of high-carbon assets.

Europe has been the pioneer of low-carbon development and the birthplace of the concepts “carbon peaking” and “carbon neutrality”. The Paris Agreement, which aimed at reducing global greenhouse gas emissions, was also initiated by Europe. Europe has a long-term clean energy practice and corresponding achievements, but a 70–100-year plan is still needed for the transition from carbon peak to carbon neutrality. As the latecomer of low-carbon transformation, China has promised to reduce the time from carbon peak to carbon neutrality. It needs not only courage but also technology breakthroughs, investment and strong policies and measures. More stringent requirements should be put forward for the carbon emissions of every region, province and industry in China. However, currently, China has not formed a transformation governance system of collaborative emission reduction development at both the regional level and the industrial level.

As the leading industry of China’s economy, the construction sector accounts for 70% of the potential of energy conservation and emission reduction in China. At present, China’s “14th Five-year Plan” is in the process of formulation. Based on the results of this paper, the following policy recommendations on carbon energy conservation and emission reduction in China’s construction industry are put forward:

(1) Indirect carbon emissions are the main source of CO₂ emissions from the construction industry, accounting for about 95% of the total emissions. Therefore, more efforts should be made to promote low-carbon production and environmental protection in the production of building materials in the future. According to the development trend, we should adopt updated low-carbon environmental protection technology, strengthen the coordination of industrial policies and innovation policies and control the carbon emissions from the upstream links of the construction industry, so as to reduce the overall carbon emissions of the industry.

(2) According to the trend analysis of the decoupling index of CO₂ emissions produced by the construction industry in five regions of China, the development trend of the construction industry is unbalanced, and the problems and contradictions are still prominent, among which the development of the Northeast and Northwest regions is relatively backwards. Considering the regional differences, the construction industry policies in the new period must adapt to the strategic needs of regional coordinated development. The provinces and cities should formulate the development goals of emission reduction of the construction industry, and the central government should establish the mechanism of regional coordinated development.

(3) Analysis with the EKC curve simulation model indicates that the carbon emissions of China’s construction industry will reach the peak in 2034, which will exceed the 2030 deadline. To meet the overall goal of reaching the peak before 2030, we must improve the annual growth rate of the per capita output value of the construction industry. Therefore, we should fully engage all stakeholders in the formulation and implementation of industrial policies, organize multiple sectors and industries involved in carbon emissions of the construction industry and establish a construction industry policy coordination committee including other relevant departments during formulation and implementation. For comprehensive emission reduction policies of the construction industry, all provinces, cities, relevant departments and key enterprises should adopt a more active low-carbon development route under the new commitment of national carbon neutrality, so as to achieve higher per capita output value of the construction industry in the future, achieve the highest carbon emission value by 2030 and strive to achieve the overall goal of carbon neutrality by 2060.

In order to achieve “carbon neutrality”, the construction industry of other countries in the world must also make long-term efforts in terms of strategic planning, policies and regulations, industry standards and social awareness. First of all, the construction industry should formulate a specific “carbon neutrality” schedule, and achieve “carbon neutrality” before the national commitment period. Second, the CO₂ emissions of all sectors of the construction industry should be measured and quantified by the life cycle assessment method. Third, it is necessary to revise the industry standards and specifications substantially because to achieve “carbon neutrality”, almost all the industry standards and specifications need to integrate the relevant requirements of energy conservation and emission reduction. Fourth, policies and regulations should be formulated to promote “carbon neutrality” and other related economic, fiscal, financial and voluntary labeling policies (especially green financial policies). Finally, publicity efforts should be made to help people realize the importance of “carbon neutrality” and encourage them to form the habits of environmental protection.

6. Conclusions and Limitations

From the perspective of the life cycle, this paper defined the scope of CO₂ emissions of China’s construction industry and established a model for calculating the carbon emissions of China’s construction industry based on IPCC. By calculating and analyzing the CO₂ emissions of the construction industry in 30 provinces and cities from 2008 to 2018, we described the trend of CO₂ emissions of the construction industry in each province. Then, the Tapio decoupling model was adopted to analyze the correlation of CO₂ emissions of the construction industry in China as a whole, the five regions and the provinces. In addition, based on the theory of EKC, the model of econometric verification analysis was constructed to study the change trend of the relationship between CO₂ emissions and the economic output value of the construction industry.

The main conclusions of this paper are as follows: (1) From 2008 to 2018, China’s provincial carbon emissions of the construction industry showed a trend of first rising and then decreasing, and there were differences in the trend of carbon emissions among different regions. (2) According to the results of the decoupling effect, there has been weak decoupling of CO₂ emissions in China and 23 provinces. However, Anhui, Fujian and Jiangxi are in the state of growth connection, indicating that the development of the construction industry in these provinces is still in the simultaneous growth of environmental quality and economy. Inner Mongolia and four provinces in Northeast China are in a state of strong decoupling, indicating that the local environmental quality has been improved together with economic growth. (3) The EKC test results show that the data analysis of 2008–2018 indicates an inverted U-shaped correlation between the per capita CO₂ emissions and the per capita output value of the construction industry, that is, in the process of increasing the per capita output value, the per capita CO₂ emission of the construction industry will first increase and then decrease. In other words, the construction industry will not achieve the peak of CO₂ emission until the turning point in 2034.

There are still some limitations in this study and, accordingly, some suggestions can be made for future research. Firstly, the calculation of direct carbon emissions is not accurate enough because the boundary definition of relevant statistical data is inconsistent, and the number of statistical categories over the years has slightly increased or decreased. As a result, only 11 kinds of carbon emissions from primary energy consumption are calculated. In future research, with a clearer boundary of the data and a wider coverage on the categories, more precise information will be obtained for direct carbon emissions. Secondly, this paper only estimates the relationship between carbon emissions and economic growth, while other greenhouse gases, such as methane, nitrous oxide and sulfur hexafluoride, have not yet been analyzed. In future research, different types of greenhouse gases can be unified with carbon dioxide equivalent as the unit of measurement so as to comprehensively explore the emissions of greenhouse gases and better understand the low-carbon economy.