Evaluating Carbon-Emission Efficiency in China’s Construction Industry: An SBM-Model Analysis of Interprovincial Building Heating

Abstract

In the pursuit of China’s ambitious carbon neutrality goals, optimizing carbon-emission efficiency within the construction sector, a significant emitter, becomes critical. This study employs a super-Slacks-Based Measure (SBM) model and a Tobit regression model to analyze buildings’ heating-related carbon emissions across China, considering urban population density, electricity usage, and building energy consumption and the influencing factors that cause differences in carbon-emission efficiency difference. The results of this study show that the average building carbon-emission efficiency of 30 provinces in China is 0.789; carbon-emission efficiency is 0.89 in the south, higher than 0.69 in the north. After excluding centralized heating emissions, the value of buildings’ carbon-emission efficiency in the northern provinces increases by 0.01, of which the buildings’ carbon-emission efficiency in Jilin Province and Ningxia Hui Autonomous Region shows positive growth, respectively, by 0.12 and 0.17. In terms of influencing factors, there is a significant positive correlation between the scientific and technological levels, the regional economic scale, and carbon-emission efficiency; however, government intervention in the economy has a negative correlation with carbon-emission efficiency. Renewable energy utilization and green-policy adoption emerge as pivotal in enhancing efficiency. The contribution of this study is to underscore the necessity of fostering renewable energy, refining energy-consumption structures, and implementing green strategies to augment buildings’ heating-related carbon-emission efficiency.

1. Introduction

The current global focal point is climate change, and China has proposed the “30·60” dual-carbon target, emphasizing its carbon-emission reduction objectives. The views of the CPC Central Committee and State Council emphasize the importance of fully and accurately implementing the new development concept to achieve a carbon peak and carbon neutrality. This involves the vigorous development of energy-saving, low-carbon buildings, and the comprehensive promotion of green, low-carbon building materials [1]. The “14th Five-Year Plan” outlines specific measures for the development of green buildings and building energy efficiency [2]. It emphasizes the implementation of energy-saving and low-carbon standards tailored to each region’s local conditions. Additionally, it sets higher requirements for the proportion of low-energy-consumption buildings and green buildings in China by 2025. According to the 2021 International Energy Agency’s (IEA) statistics, the operation of buildings accounts for 30% of the global final energy consumption, with carbon emissions representing 27% of the total emissions in the energy sector [3]. According to the “China Building Energy Consumption and Carbon Emission Research Report (2022)”, the building sector contributed 50.9% of the country’s total carbon emissions in 2020 [4]. Furthermore, apart from carbon emissions, buildings also make a significant contribution to other forms of pollution, as the building and construction sector is responsible for producing over 10% of polluting particulate matter [5,6,7,8]. Additionally, the introduction of district heating in the northern regions of China during winter leads to the combustion of fuel, resulting in environmental challenges [9]. Moreover, the operational energy consumption of civil buildings in China constitutes 20% to 25% of the nation’s total energy consumption. By the end of 2019, the collective energy consumption for centralized heating in northern towns and cities amounted to approximately 200 million tons annually, representing 21% of the overall building-operation energy consumption. It is projected that by 2030, the building area in China’s northern towns and cities will expand to 15.5 billion m². If the current heating energy intensity is maintained, a single heating item will consume 250 million tons of standard coal per year [10,11,12,13].

Carbon emissions from buildings’ heating have received much attention, and China’s proposed dual-carbon target underscores their importance. There are many uncertainties under China’s dual-carbon target regarding buildings’ heating-related carbon emissions, including the following: (1) Is there a regional difference between China’s inter-provincial buildings’ heating-related carbon emissions and buildings’ heating-related carbon emissions? (2) What are the influencing factors of buildings’ heating-related carbon emissions? To address the above questions, this study explores the spatial characteristics and influencing factors of buildings’ carbon emissions in China and gains a deeper understanding of the mechanism required for reducing buildings’ heating-related carbon emissions, and provides suggestions for reducing buildings’ heating-related carbon emissions and achieving China’s dual-carbon goal in the future. It is extremely important to study carbon emissions from China’s building heating methods and formulate policies accordingly.

2. Literature Review

With the proposal of China’s dual-carbon goal, there has been a shift in research focus towards carbon-emission reduction. The construction industry, being the primary carbon emission sector in China, is now a key area of interest for improving the efficiency of emission reduction and reducing carbon dioxide emissions. Addressing the long-term inefficiency of buildings’ carbon emissions is currently a significant challenge [14]. The predominant focus of existing research, both nationally and internationally, is on the spatial distribution and driving factors of carbon emissions from buildings.

In this research on carbon emissions in the construction industry, the assessment of carbon-emission efficiency, and related concepts, is predominantly conducted within the framework of a total factor productivity analysis. This framework is characterized by the expression of carbon-emission efficiency as the ratio of inputs to outputs.

Existing research utilizing the super-Slacks-Based Measure (SBM) model examined the spatial distribution of carbon emissions in the construction industry [15,16]. These studies suggested that the efficiency of carbon emissions in construction varied regionally and was in line with the level of economic development [17]. Alternatively, commencing from the determinants of carbon-emission efficiency in China’s construction sector [18], pertinent research determined that population growth and energy consumption are the primary factors contributing to the enhancement of carbon-emission efficiency in China’s construction industry [19]. Tian et al. [20] conducted an analysis of scenarios aimed at reducing energy consumption for building heating and CO₂ emissions at the community level. Li et al. [21] investigated the impact of renewable energy utilization on reducing carbon emissions in the construction sector. Rui et al. [22] examined the theoretical mechanisms for reducing carbon emissions in construction, considering factors such as population, building area, and buildings’ energy consumption. Jiang et al. [23] conducted an analysis of CO₂ emissions in Jiangsu province, focusing on energy structure, energy intensity, and investment.

Simultaneously, other scholars suggested examining the carbon emissions of China’s construction industry through the lens of building energy consumption. They determined that China’s per capita area energy consumption significantly influences carbon emissions [24]. Don et al. [25] conducted an evaluation of the socio-economic and environmental impacts associated with energy consumption for heating buildings using a Carbon Capture, utilization, and Storage (CCUS) system. Duan et al. [26] carried out a study on carbon emissions resulting from heating operations in buildings. The heating sector contributed to 55.74% of the total carbon emissions, highlighting the significance of energy conservation and low-carbon practices as the most effective solution for mitigating carbon emissions. Yan et al. [27] investigated the correlation between building energy consumption and carbon emissions. Li et al. [28] and Teng et al. [29] determined that the utilization of renewable-energy generation exhibited the potential to mitigate carbon emissions. In accordance with China’s dual-carbon goal, it is imperative to enhance energy-saving technology and increase the efficiency of buildings’ energy consumption [30]. Based on the above research findings, the construction industry produces significant amounts of carbon emissions through building heating. Reducing carbon emissions from building heating requires energy-saving and low-carbon measures. To develop more effective emission-reduction strategies, it is necessary to understand the influencing factors and emission-reduction mechanisms of buildings’ heating-related carbon emissions. Based on the super efficiency of the SBM model and the development background of China’s dual-carbon policy, this research examines the carbon emissions of interprovincial buildings in China, filling the gap in heating-related carbon emissions in the Chinese construction industry, taking into account factors such as population density, energy intensity, electricity consumption, and comprehensive heating factors.

3. Research Methods and Data Selection

3.1. Research Methodology

Data envelopment analysis (DEA) is a quantitative method that utilizes a linear programming theory to evaluate the relative efficiency of similar units based on both input and output indicators. DEA offers a significant advantage in data analysis when handling multiple-indicator inputs and outputs. The DEA method and its model were introduced by Charnes et al. [31]. The DEA model eliminates the need for subjective weight assumptions on the data, as it can automatically derive the optimal weight from the actual data of the decision unit, thus enhancing objectivity. It exhibits a high degree of objectivity. The use of DEA for efficiency evaluation yields a wealth of management information with profound economic significance and a strong foundation in economics. Consequently, the research in the field of DEA has garnered significant attention from scholars. The conventional DEA models encompass the Charnes, Cooper, and Rhodes (CCR) model [31] and the Banker, Charnes, and Cooper (BCC) model [32]. In contrast to the BCC model, the CCR model assumes that all building units are technically efficient, so it is more appropriate for evaluating homogeneous groups of buildings. A BCC model can evaluate heterogeneous building communities by taking into account differences in technical efficiency.

In the DEA model, a large number of indicators can result in multiple DMUs (decision-making units) exhibiting an effective state (efficiency value of 1), making it difficult to compare the efficiency values of these effective DMUs. Andersen and Petersen [33] proposed a model to distinguish the efficiency of effective DMUs, known as the super-efficiency model, in order to more accurately determine the magnitude of efficiency. Meanwhile, in order to enhance the incorporation of relaxation considerations in the calculation of DMU efficiency, this study employs the super-SBM method proposed by Tone et al. [34] for measuring stage efficiency. The super-SBM model not only mitigates bias resulting from the selection of radial direction and angle, but also provides a comprehensive evaluation of the effective DUMs. The super-efficiency DEA model is suitable for buildings’ heating-related carbon-emission-efficiency measurement, which can comprehensively consider multiple input and output factors, take into account the variability of technical efficiency and environmental constraints, and provide an efficiency enhancement analysis, so as to provide a scientific basis and decision-making support for the reduction of buildings’ heating-related carbon emissions. As a result of the super efficiency SBM model, it is possible to calculate the carbon-emission efficiency of building heating. As well as providing the scientific basis and decision support for reducing the carbon emissions of buildings’ heating, it can analyze multiple input and output factors, take into account technical efficiency and environmental constraints, and provide an efficiency-improvement analysis.

The model is developed as follows:

$$\mathit{min}\rho =\frac{1+\frac{1}{m}{\sum }_{i=1}^m{s}_i^{-}/x_{ik}}{1-\frac{1}{s}{\sum }_{r=1}^s{s}_r^{+}/y_{rk}};$$

(1)

$$s.t. {\sum }_{j=1,j\ne k}^n{x}_{ij}{\gamma }_j-s_i^{-}\le x_{ik};$$

(2)

$${\sum }_{j=1,j\ne k}^n{y}_{rj}{\gamma }_j+s_r^{+}\ge y_{rk};$$

(3)

$$\left\{\begin{array}{c}\gamma , s^{-}, s^{+}\ge 0,\\ i=\mathrm{1,2},\cdots ,m,\\ r=\mathrm{1,2},\cdots ,q,\\ j=\mathrm{1,2},\cdots ,n \left(j\ne k\right).\end{array}\right.;$$

(4)

where $\rho$ represents the efficiency calculated by the model; $\mathrm{x}$ and $\mathrm{y}$ represent input and output factors, respectively; $m$ and $s$ represent the number of input and output indicators, respectively; $k$ represents the production period; $i$ and $r$ represent the input decision unit and the output decision unit, respectively; $s^{-}$ and $s^{+}$ represent the input slack and the output slack, respectively; and $\mathrm{j}$ represents the weight vector. In the DEA model’s solution, the categorization of a DMU is as follows: if $\rho$ = 1 and $s^{-}=s^{+}$ = 0, the DMU is deemed DEA-efficient; if $\rho$ = 1 and $s^{-}\ne s^{+}\ne$0, the DMU is considered weakly DEA-efficient; if $\rho$ < 1, the DMU is considered DEA-inefficient.

3.2. Tobit Regression Model

From the above super-efficiency SBM-DEA model, we know that the range of carbon-emission efficiency values obtained is limited to 0 on the left side, and there is no limited point on the right side. The structural equation of the Tobit model was given as

$$y_i^{*}=\alpha +X\beta +u_i$$

(5)

$$u_i~N\left(0,{\delta }^2\right)$$

(6)

$$y_i=\left\{\begin{array}{c}{y}_i^{*} if y_i^{*}>0\\ 0 if y_i^{*}\le 0\end{array}\right.$$

(7)

In the equation, $X$ is the explanatory variable; $y_i^{*}$ is the latent variable; $y_i$ is a limited dependent variable; $\alpha$ is an intercept term vector; $\beta$ stands for the correlation coefficient; $u_i$ is the random error with the distribution of $N(0,{\delta }^2);$ and $i$ stands for the $i$th DUM.

3.3. Indicator Selection

The research period for this paper is from 2005 to 2021, and decision-making units in 30 provinces and cities in China were selected for the evaluation of buildings’ carbon-emission efficiencies. Tibet, Hong Kong, Macao, and Taiwan were excluded from consideration due to data limitations. Considering the availability and comprehensiveness of the data, this paper selects indicators based on the previous literature [35,36,37] to establish a relatively rational set of provincial buildings’ heating-related carbon-emission indicators. The specific variables are outlined in

Table 1. Variable description in the study.

Norm	Variable	Description
Inputs	Urban population density (I1)	Year-end urban and town resident population divided by the provincial (city) area (persons/km2).
	Electricity consumption (I2)	Total societal electricity consumption (billion kilowatt-hours).
	Energy consumption (I3)	Operational-phase energy consumption in buildings, primarily from sectors like wholesale, retail, accommodation, catering, others, and residential living, including centralized winter heating in the northern region (tons of standard coal) [4].
Expected outputs	Per capita disposable income (O1)	Per capita sum of final consumption expenditures, non-obligatory expenditures, and savings (CNY).
	Construction area (O2)	Area of commercial properties sold in the real estate sector (10,000 m2).
Non-expected outputs	CO2 emissions (O3)	CO2 emissions during the operational phase of buildings (tons).

.

The data were sourced from the China Statistical Yearbook [38], China Energy Statistical Yearbook [39], China Building Statistical Yearbook [40], and China Urban and Rural Construction Statistical Yearbook [41] from 2005 to 2021. Buildings’ energy consumption and carbon dioxide emissions cannot be directly measured. Therefore, this paper calculates carbon dioxide emissions based on the carbon-emission coefficients provided in “2006 National Greenhouse Gas Emission Inventory of Intergovernmental Panel on Climate Change (IPCC)” [42]. The equation for this calculation is expressed as follows:

$${CO}_2={\Sigma }_{i=1}^3{E}_i\times {NCV}_i\times {CEF}_i\times {COF}_i\times \left(\frac{44}{12}\right)$$

(8)

Buildings’ energy consumption is determined as follows:

$${{\displaystyle E}}_t={{\displaystyle \Sigma }}_{i=1}^5{{\displaystyle a}}_i{{\displaystyle ei}}_t{{\displaystyle b}}_i$$

(9)

where ${\mathrm{E}}_{\mathrm{t}}$ represents the energy consumption of the building at year $t$; $i$ represents the type of energy source; $a_i$ represents the standard coal-conversion factor for energy type $i$; $e{i}_t$ represents the quantity of energy source $i$ used in year $t$; $b_i$ represents the proportion of each energy source attributed to buildings’ energy consumption. Notably, apart from diesel, which has a $b_i$ ratio of 4%, the remaining energy types (coal, liquefied petroleum gas, and natural gas) are assigned a bi-ratio of 1 [43].

During the winter season, centralized heating in northern China primarily involves the use of hot water and steam. The data regarding the total amount of centralized heat supply is obtained from the China Urban and Rural Construction Statistical Yearbook [40]. The equation used to calculate the energy consumption of this component is expressed as follows:

$${{\displaystyle E}}_{ht}={\displaystyle \sum \left(e_{1t}+\left(e_{2t}\right)\right)}\ast {{\displaystyle a}}_0$$

(10)

where $e_{1t}$ and $e_{2t}$ represent the centralized heat supply of hot water and steam in year $t$, respectively, and $a_0$ represents the conversion coefficient, with a value of 29,307 kJ/kg of standard coal.

4. Results and Discussion

4.1. DEA Result

Utilizing DEA Solver software (Dea solver Pro 5.0), the input–output analysis is conducted for each province to determine the carbon-emission efficiency of the buildings. Furthermore, in order to delineate the variations in heating across China, the regional carbon-emission efficiency is further segmented based on the provincial and municipal regional divisions in China. This paper delineates the northern region by using Qinling Mountains–Huaihe River as the boundary and the provincial administrative unit as the basic unit, taking into account economic and geographic factors [44,45,46]. The northern region includes 15 provinces, municipalities directly under the central government, and the following autonomous regions: Heilongjiang, Jilin, Liaoning, Inner Mongolia, Hebei, Beijing, Tianjin, Shaanxi, Shanxi, Ningxia, Gansu, Qinghai, Shandong, Henan, and Xinjiang. The southern region comprises 15 provinces, municipalities directly under the central government, or autonomous regions, including Jiangsu, Zhejiang, Shanghai, Anhui, Hubei, Hunan, Jiangxi, Sichuan, Chongqing, Guizhou, Yunnan, Guangxi, Fujian, Guangdong, and Hainan. The measured values are presented in

Table 2. Provincial buildings’ carbon-emission efficiency results from 2005 to 2021.

Province	2005	2006	2007	2008	2009	2010	2011	2012	2013	2014	2015	2016	2017	2018	2019	2020	2021	Average
Heilongjiang	0.39	0.43	0.48	0.54	0.49	0.57	0.61	0.64	0.59	0.49	0.44	0.43	0.43	0.42	0.41	0.38	0.37	0.48
Beijing	1.90	1.73	1.50	1.33	1.40	1.30	1.24	1.24	1.24	1.24	1.24	1.36	1.30	1.30	1.47	1.46	1.47	1.39
Liaoning	0.44	0.51	0.67	1.08	1.03	1.14	1.14	1.15	1.11	0.92	0.79	0.70	0.66	0.65	0.63	0.63	0.61	0.82
Jilin	0.57	0.63	0.75	1.05	0.77	0.83	0.74	0.72	0.66	0.53	0.53	0.64	0.59	0.85	1.00	1.00	0.77	0.74
Inner Mongolia	0.38	1.00	0.54	0.63	0.62	0.65	0.75	1.01	1.06	1.08	0.68	0.62	0.50	0.48	0.56	1.01	0.48	0.71
Hebei	0.26	0.31	0.33	0.40	0.40	0.53	0.59	0.52	0.55	0.59	0.56	0.59	0.54	0.47	0.48	0.51	0.50	0.48
Qinghai	1.06	1.07	1.06	1.05	1.05	1.05	1.05	1.05	1.04	1.05	1.05	1.07	1.06	1.06	1.07	1.07	1.06	1.06
Shandong	0.48	0.58	1.01	1.09	1.03	1.09	1.11	1.04	1.11	1.14	1.09	1.09	1.09	1.11	1.09	1.08	1.07	1.02
Ningxia	0.47	0.47	0.47	1.10	1.09	1.09	1.12	1.12	1.10	1.11	1.12	1.12	1.09	1.10	0.44	0.50	0.49	0.88
Henan	0.32	0.35	0.40	0.47	0.48	0.58	0.63	0.55	0.63	0.71	0.68	1.03	1.03	1.02	1.00	0.79	0.74	0.67
Tianjin	0.59	0.66	0.70	0.73	0.72	0.69	0.68	0.67	0.69	0.65	0.65	0.74	0.60	0.55	0.63	0.62	0.61	0.66
Shaanxi	0.23	0.28	0.31	0.41	0.41	0.47	0.53	0.48	0.50	0.50	0.51	0.50	0.50	0.52	0.49	0.52	0.48	0.45
Shanxi	0.22	0.26	0.29	0.37	0.31	0.32	0.32	0.32	0.34	0.35	0.32	0.40	0.38	0.38	0.37	0.40	0.43	0.34
Gansu	0.20	0.23	0.26	0.31	0.26	0.26	0.26	0.29	0.32	0.31	0.33	0.36	0.32	0.35	0.38	0.40	0.43	0.31
Xinjiang	0.21	0.26	0.28	0.36	0.36	0.39	0.39	0.30	0.39	0.38	0.38	0.42	0.31	0.29	0.26	0.28	0.32	0.33
Northern regional average	0.51	0.58	0.60	0.73	0.69	0.73	0.74	0.74	0.75	0.74	0.69	0.74	0.69	0.70	0.68	0.71	0.66	0.69
Hainan	1.32	1.34	1.37	1.40	1.40	1.45	1.47	1.51	1.58	1.62	1.52	1.54	1.55	1.48	1.42	1.40	1.41	1.46
Chongqing	1.01	1.04	1.18	1.18	1.15	1.16	1.16	1.12	1.11	1.17	1.17	1.24	1.14	1.14	1.11	1.13	1.11	1.14
Shanghai	1.14	1.14	1.23	1.17	1.13	1.12	1.11	1.11	1.11	1.11	1.11	1.12	1.11	1.11	1.13	1.13	1.13	1.13
Jiangsu	1.12	1.15	1.18	1.18	1.19	1.12	1.06	1.09	1.11	1.09	1.12	1.12	1.11	1.12	1.13	1.11	1.12	1.13
Sichuan	1.02	1.03	1.00	0.68	1.01	1.03	1.04	1.01	1.02	1.03	1.04	1.01	1.03	1.05	1.08	1.08	1.11	1.02
Zhejiang	1.01	1.01	1.01	0.87	1.01	1.01	1.02	1.03	1.02	1.03	1.02	1.01	1.01	1.00	1.00	1.02	1.02	1.01
Fujian	0.70	0.74	0.76	0.76	0.86	0.89	0.98	1.01	1.02	1.01	1.00	1.01	1.01	1.01	1.00	1.01	1.00	0.93
Guangxi	0.55	0.66	0.82	0.78	0.78	0.77	0.76	0.76	0.79	0.79	0.75	1.00	0.83	1.00	1.01	1.00	0.76	0.81
Guangdong	0.81	0.70	0.65	0.69	0.65	0.77	0.80	0.79	0.81	0.85	1.00	1.03	1.01	0.80	0.73	0.76	0.73	0.80
Anhui	0.41	0.46	0.51	0.65	0.65	0.66	0.69	0.70	0.79	0.84	0.74	1.02	0.89	1.00	0.82	0.84	1.00	0.75
Hunan	0.40	0.44	0.50	0.63	0.57	0.66	0.69	0.69	0.74	0.70	0.72	1.02	0.76	0.84	0.83	0.82	0.77	0.69
Hubei	0.41	0.47	0.49	0.53	0.53	0.62	0.68	0.65	0.72	0.79	0.76	0.85	0.78	0.82	0.80	0.70	0.74	0.67
Jiangxi	0.51	0.49	0.53	0.58	0.63	0.63	0.61	0.63	0.74	0.73	0.67	0.75	0.72	0.70	0.75	0.75	0.84	0.66
Yunnan	0.52	0.52	0.55	0.62	0.63	0.67	0.66	0.64	0.73	0.63	0.62	0.70	0.68	0.67	0.69	0.69	0.66	0.64
Guizhou	0.36	0.36	0.38	0.39	0.43	0.47	0.48	0.49	0.50	0.52	0.58	0.64	0.59	0.62	0.66	0.68	0.70	0.52
Southern regional average	0.75	0.77	0.81	0.81	0.84	0.87	0.88	0.88	0.92	0.93	0.92	1.00	0.95	0.96	0.94	0.94	0.94	0.89
Nationwide	0.6338	0.68	0.71	0.77	0.77	0.80	0.81	0.81	0.84	0.83	0.81	0.87	0.82	0.83	0.81	0.83	0.80	0.79

. The calculated values and trends after excluding the influence of northern heating are shown in

Table 3. Carbon efficiency of buildings excluding central heating CO₂ emissions in northern regions.

Province	2005	2006	2007	2008	2009	2010	2011	2012	2013	2014	2015	2016	2017	2018	2019	2020	2021	Average
Heilongjiang	0.39	0.44 ↑	0.49 ↑	0.54	0.49	0.58 ↑	0.61	0.64	0.59	0.50 ↑	0.44	0.43	0.43	0.42	0.41	0.39 ↑	0.38 ↑	0.48
Beijing	1.90	1.74 ↑	1.49 ↓	1.31 ↓	1.38 ↓	1.29 ↓	1.24	1.24	1.24	1.24	1.24	1.29 ↓	1.29 ↓	1.29 ↓	1.45 ↓	1.43 ↓	1.44 ↓	1.38 ↓
Liaoning	0.44	0.51	0.68 ↑	1.08	1.03	1.14	1.15 ↑	1.16 ↑	1.12	0.94 ↑	0.82 ↑	0.69 ↓	0.67 ↑	0.66 ↑	0.63	0.64 ↑	0.62 ↑	0.82
Jilin	0.64 ↑	1.00 ↑	1.00 ↑	1.06 ↑	0.79 ↑	0.87 ↑	0.77 ↑	0.74 ↑	0.69 ↑	0.55 ↑	0.56 ↑	0.66 ↑	0.61 ↑	0.89 ↑	1.03 ↑	1.03 ↑	1.02 ↑	0.82 ↑
Inner Mongolia	0.34 ↓	0.45 ↓	0.50 ↓	0.60 ↓	0.56 ↓	0.62 ↓	0.75	0.78 ↓	1.05 ↓	1.06 ↓	0.65 ↓	0.54 ↓	0.50	0.48	0.56	1.01	0.45 ↓	0.64 ↓
Hebei	0.27 ↑	0.31	0.34 ↑	0.40	0.40	0.53	0.59	0.52	0.54 ↓	0.59	0.57 ↑	0.56 ↓	0.54	0.48 ↑	0.48	0.51	0.50	0.48
Qinghai	0.47 ↓	1.04 ↓	1.04 ↓	1.00 ↓	1.02 ↓	1.01 ↓	1.03 ↓	1.03 ↓	1.01 ↓	1.03 ↓	1.03 ↓	1.03 ↓	1.05 ↓	1.03 ↓	1.06 ↓	1.06 ↓	1.05 ↓	1.00 ↓
Shandong	0.48	0.58	1.01	1.09	1.04 ↑	1.09	1.11	1.04	1.11	1.15 ↑	1.10 ↑	1.08 ↓	1.10	1.12 ↑	1.10 ↑	1.09 ↑	1.08 ↑	1.02
Ningxia	1.05 ↑	1.02 ↑	1.06 ↑	1.15 ↑	1.12 ↑	1.13 ↑	1.14 ↑	1.14 ↑	1.13 ↑	1.13 ↑	1.15 ↑	1.12	1.11 ↑	1.13 ↑	0.76 ↑	0.60 ↑	0.84 ↑	1.05 ↑
Henan	0.32	0.36 ↑	0.40	0.47	0.48	0.58	0.63	0.55	0.63	0.72 ↑	0.68	1.02 ↓	1.02 ↓	1.02	1.00	0.80 ↑	0.74	0.67
Tianjin	0.59	0.66	0.71 ↑	0.74 ↑	0.72	0.69	0.68	0.67	0.69	0.65	0.65	0.74	0.60	0.56 ↑	0.63	0.62	0.62 ↑	0.66
Shaanxi	0.23	0.28	0.31	0.40 ↓	0.40 ↓	0.47	0.53	0.48	0.50	0.50	0.51	0.48 ↓	0.51 ↑	0.52	0.49	0.52	0.48	0.45
Shanxi	0.22	0.26	0.29	0.37	0.31	0.32	0.32	0.32	0.34	0.35	0.33 ↑	0.35 ↑	0.38	0.38	0.37	0.40	0.44 ↑	0.34
Gansu	0.20	0.23	0.26	0.31	0.26	0.26	0.26	0.29	0.32	0.31	0.33	0.35 ↓	0.32	0.35	0.38	0.41 ↑	0.44 ↑	0.31
Xinjiang	0.21	0.27 ↑	0.28	0.37 ↑	0.38 ↑	0.40 ↑	0.40 ↑	0.31 ↑	0.39	0.38	0.39 ↑	0.36 ↓	0.32 ↑	0.30 ↑	0.26	0.29 ↑	0.32	0.33
Northern regional average	0.52 ↑	0.61 ↑	0.66 ↑	0.73	0.69	0.73	0.74	0.73 ↓	0.76 ↑	0.74	0.70 ↑	0.71 ↓	0.70 ↑	0.71 ↑	0.70 ↑	0.72 ↑	0.69 ↑	0.70 ↑

. In order to better demonstrate the inter-provincial differences in building heating, the distribution map of China’s buildings’ carbon-emission efficiency values is shown in Figure 1. (The right-hand column of the figure shows the national carbon efficiency of buildings excluding the effects of central heating). The average regional carbon efficiency is shown in Figure 2.

The combination of Table 2 and Figure 1 reveals that the buildings’ carbon-emission efficiency in Jiangsu, Zhejiang, Shanghai, Qinghai, Sichuan, and Chongqing consistently surpasses that of other regions in the country across all years. Additionally, these regions exhibit smaller annual fluctuations. The distribution map of China’s buildings’ carbon-emission efficiency values indicates that, overall, the northern region has lower building carbon-emission efficiency compared to the southern region. This phenomenon could be attributed to the significant reliance on fossil fuels in the northern region, coupled with the low efficiency of carbon emissions from buildings during winter heating. As shown in Table 3 and Figure 1, after excluding the carbon dioxide emissions caused by central heating in the northern region, it can be found that the average carbon emission efficiency in the northern region has increased, with the average efficiency in Ningxia reaching an effective level.

Overall, there has been a consistent increase in national construction’s carbon emission efficiency from 2005 to 2021, and the annual change trend of carbon-emission efficiency in the construction industry is similar in both the northern and southern regions. Between 2005 and 2008, there was a general upward trend in carbon emissions, primarily attributed to the rapid economic development of China during this period and the substantial investment in infrastructure construction, leading to an increase in carbon emissions. Following the economic crisis of 2008, economic growth decelerated, while construction activities continued to rise. Following the economic crisis in 2008, China experienced a deceleration in economic growth and construction speed, a decrease in carbon-emission efficiency, and fluctuating emission efficiency after 2008. The average carbon-emission efficiency and ranking of buildings from 2005 to 2021 is shown in

Table 4. Average carbon-emission efficiency and ranking of buildings from 2005 to 2021.

Province	Average Efficiency Value	Rank	Province	Average Efficiency Value	Rank	Province	Average Efficiency Value	Rank
Hainan	1.4576	1	Ningxia	0.8827	11	Jiangxi	0.6638	21
Beijing	1.3941	2	Liaoning	0.8151	12	Tianjin	0.6577	22
Chongqing	1.1369	3	Guangxi	0.8121	13	Yunnan	0.6396	23
Shanghai	1.1288	4	Guangdong	0.7987	14	Guizhou	0.5202	24
Jiangsu	1.1254	5	Anhui	0.7451	15	Hebei	0.4779	25
Qinghai	1.0569	6	Jilin	0.7419	16	HeilongJiang	0.4758	26
Sichuan	1.0175	7	Inner Mongolia	0.7090	17	Shaanxi	0.4487	27
Shandong	1.0172	8	Hunan	0.6929	18	Shanxi	0.3392	28
Zhejiang	1.0068	9	Henan	0.6711	19	Xinjiang	0.3281	29
Fujian	0.9272	10	Hubei	0.6663	20	Gansu	0.3105	30

.

Only nine provinces and cities, including Hainan, Beijing, Chongqing, Shanghai, Jiangsu, Qinghai, Sichuan, Shandong, and Zhejiang, have achieved an effective outcome. Hainan Province exhibits the highest average building-emission efficiency due to its tropical monsoon climate, resulting in an average annual temperature exceeding 20 °C and abundant light. The region also possesses various types of renewable energy resources, leading to the gradual replacement of traditional fossil energy with clean energy. In 2021, wind, solar, hydro, and other forms of power generation accounted for 32.33% of the total power generation. Qinghai Province demonstrates a superior performance in carbon-emission efficiency compared to other northwestern provinces, a result that can be attributed to its geomorphology and climate. Qinghai Province features a typical plateau continental climate characterized by low precipitation, dryness, strong winds, cold temperatures, extended periods of sunshine, and ample solar- and wind-energy resources. Consequently, residential buildings in this region do not heavily depend on fossil energy for heating.

Beijing, Chongqing, and Shanghai are three municipalities directly under the central government, and they are leading the efforts in energy conservation and emission-reduction publicity work and technology in the country. In 2014, “Interim Measures for the Management of Financial Incentive Funds for Developing Green Buildings” and “Promoting the Construction of Green Ecological Demonstration Areas in Beijing” were issued [47]. These measures established “Beijing Development of Green Buildings and Promoting the Construction of Green Ecological Demonstration Areas Incentive Funds”, which aims to provide incentives for public and residential building projects that have obtained the two- or three-star green building-operation label. The audited projects include public and residential buildings, as well as green eco-demonstration zones, that have met the specified criteria. As of December 2019, the total construction area of projects that have adopted green building standards in Beijing is close to 250 million m². Among these, approximately 75.86 million m² of green construction area has been finished. In the city, 409 projects have obtained certification with green building labels, encompassing a construction area of 47.18 million m². Furthermore, 93% of the construction area belongs to projects with a two-star rating or higher, including 52 operational labels and 357 design labels. In 2020, Beijing Municipal Commission of Housing and Urban-Rural Development (BCHURD), in collaboration with Municipal Planning and Natural Resources Commission (MPNRC) and the Municipal Finance Bureau (MFB), released “Interim Measures for the Management of Municipal Reward Funds for Projects” [47] related to Beijing’s assembly buildings, green buildings, and green ecological demonstration zones, with the aim of promoting their high-quality development [48]. By the conclusion of the “13th Five-Year Plan”, Chongqing Municipality has overseen the execution of 24,413,500 m² of high-star green buildings and 106,427,700 m² of green ecological residential communities. The percentage of green buildings in the urban areas of the city has risen to 57.2% in terms of new construction. The total area of energy-efficient buildings has reached 679 million m², and the utilization of renewable energy buildings has surpassed 15 million m². The installed area of renewable energy applications has surpassed 15 million m². Meanwhile, the Chongqing Municipality has actively promoted the advancement of sustainable buildings, investigating near-zero energy usage, low-carbon (zero-carbon) structures, and constructing the inaugural “zero-carbon cabin” in Chongqing in 2020. Shanghai has provided policy and financial incentives for ultra-low-energy buildings, carried out energy-saving renovations on public buildings covering an area of 16 million m², and advanced 42 ultra-low-energy building projects covering approximately 3.5 million m². The city’s system for monitoring buildings’ energy consumption encompasses 2100 buildings and 99 million m². These policies and measures have significantly contributed to enhancing the efficiency of reducing carbon emissions in buildings.

4.2. Influence Factors of Carbon-Emission Efficiency Based on the Tobit Regression

Based on the regional differences in the carbon-emission efficiency of centralized heating in buildings, this research uses the Tobit regression model to analyze its influencing factors. Based on the existing literature, factors such as science and technology levels, regional economic scale, government intervention, and industrial structure are selected as factors that affect the carbon-emission efficiency of centralized heating in buildings.

(1)

Science and technology levels:

The differences in scientific and technological levels lead to differences in centralized heating technology, equipment, and modes, affecting carbon-emission efficiency [49,50,51,52,53,54]. The level of science and technology is often reflected in the level of research and development investment. Jun and Li et al. pointed out in 2023 that increasing investment in technology can significantly improve the carbon-emission efficiency of urban industries [55]. Wang and Zhao et al. found in 2019 that R & D investment plays an important role in carbon dioxide reduction [56]. This article uses the proportion of local fiscal science and technology expenditure to GDP as an indicator to measure the level of science and technology.

(2)

Regional economic scale:

Different regions have different industrial development characteristics and economic levels. This research uses the proportion of regional GDP to national GDP to represent the scale of regional economy

(3)

Government intervention:

The government’s macroeconomic regulation policies can affect resource allocation and industrial transfer between regions, indirectly affecting carbon emissions. This article uses the proportion of local fiscal expenditure to GDP as an indicator of government intervention level.

(4)

Industrial structure:

The intensity of energy consumption varies significantly among different industries, and the industrial structure is a factor that affects the carbon-emission efficiency of centralized heating in buildings. Therefore, this research takes the proportion of local added value of the tertiary industry to GDP as an indicator of industrial structure.

The specific content of the influencing factors is shown in

Table 5. Influencing factors.

Explanatory Variable	Variable’s Definition and Unit	Reference
Science and technology levels (X1)	The proportion of the local R & D expenditure to GDP (%)	[50,51,54]
Size of the regional economy (X2)	The proportion of regional GDP to national GDP (%)	[57]
Government intervention (X3)	The proportion of local fiscal expenditure to GDP (%)	[58]
Industrial structure (X4)	The proportion of the local added value of the tertiary industry to GDP (%)	[58]

:

4.3. Unit Root Test for Panel Data

Before conducting Tobit regression analysis, we used LLC, IPS, and Fisher’s long panel data unit root test to test variable stability and prevent pseudo-regression caused by directly modeling non-stationary data. According to

Table 6. Panel unit root test results.

	LLC	IPS	Fisher-ADF
y	−11.1486 ***	−3.2413 ***	245.489 ***
X1	−9.9437 ***	−3.3552 ***	127.8931 ***
X2	−1.5752	4.2066	147.4586 ***
X3	−3.9404 ***	0.6251	158.8104 ***
X4	−3.4821 ***	2.0596	141.5935 ***
∆y	−25.1148 ***	−9.8411 ***	293.6971 ***
∆X1	−31.1545 ***	−11.3149 ***	226.1616 ***
∆X2	−9.3671 ***	−6.1481 ***	218.3934 ***
∆X3	−14.7605 ***	−8.7880 ***	211.4984 ***
∆X4	−10.9716 ***	−6.7182 ***	190.6354 ***

*** representing variables significant at 1%.

, all four variables become stable at the 1% significance level. The null hypothesis of “existing unit roots” is rejected within a 1% pseudo-regression after taking the first difference.

4.4. Tobit Regression Results

In summary, this article evaluates the impact of the above influencing factors on buildings’ carbon dioxide-emission efficiency, and constructs a Tobit regression model as ${\mathrm{e}\mathrm{f}\mathrm{f}\mathrm{i}\mathrm{c}\mathrm{i}\mathrm{e}\mathrm{n}\mathrm{c}\mathrm{y}}_{\mathrm{i},\mathrm{t}}={\mathsf{\beta }}_0+{\mathsf{\beta }}_1{\mathrm{X}}_{\mathrm{i}\mathrm{t}}+{\mathsf{\beta }}_2{\mathrm{X}}_{\mathrm{i}\mathrm{t}}+{\mathsf{\beta }}_3{\mathrm{X}}_{\mathrm{i}\mathrm{t}}+{\mathsf{\beta }}_4{\mathrm{X}}_{\mathrm{i}\mathrm{t}}+{\mathrm{u}}_{\mathrm{i}\mathrm{t}}$. The results of each parameter calculated through StataMP 15 are as follows (see

Table 7. Tobit regression results.

Efficiency	Coef.	Std.Err	z	P > |z|	[95% Conf. Interval]
X1	11.719	4.099291	2.86	0.004	3.684532	19.75346
X2	7.5717	1.583584	4.78	0.000	4.467931	10.67547
X3	0.652528	0.1380901	4.73	0.000	0.3818765	0.9231796
X4	0.1115986	0.133429	0.84	0.403	−0.1499175	0.3731147
_cons	0.276892	0.0982596	2.82	0.005	0.0843068	0.4694773
sigma_u	0.2849325	0.039263	7.26	0.000	0.2079785	0.3618865
sigma_e	0.1208147	0.0039133	30.87	0.000	0.1131447	0.1284846
rho	0.8476115	0.036809			0.764255	0.9086404

).

According to the regression coefficient of 11.719, the level of science and technology is directly related to the carbon-emission efficiency of centralized heating in buildings at the 1% level. A building’s centralized heating system is more efficient at reducing carbon emissions the higher its level of science and technology. The regional economic level is significant at the 1% level, with a regression coefficient of 7.5717. This indicates that the higher the regional economic level, the more helpful it is to reduce regional buildings’ carbon emissions. Based on the super efficiency SBM-DEA, economically developed regions like Beijing, Shanghai, Jiangsu Province, etc., have a higher efficiency of carbon emissions, while economically underdeveloped regions like Gansu, Inner Mongolia, etc., have a lower efficiency of carbon emissions. Government intervention has a significant negative impact on buildings’ carbon-emission efficiency. Excessive government intervention in the economy is not conducive to improving carbon-emission efficiency. There is a non-significant negative correlation between industrial structure and buildings’ carbon-emission efficiency, which only affects carbon-emission efficiency to a certain extent.

Consistent with existing research findings, in regions rich in renewable energy, there is enormous potential for buildings’ carbon-emission reduction [59], and buildings’ heating dependence on fossil fuels is relatively low. The policy has promoted the transformation of buildings towards low carbon [60], and buildings’ carbon-emission changes are consistent with economic trends [61]. It is possible to reduce carbon emissions in regions abundant in renewable energy. The carbon emissions of the construction industry are also influenced by government policy support and economic development. Overall, in regions rich in renewable energy, buildings’ heating dependence on fossil fuels is relatively low. Policies play a negative role in influencing buildings’ carbon emissions in some cases, which is inconsistent with the conclusion that policies play a positive role in buildings’ carbon emissions [62,63].

5. Conclusions and Recommendations

This study examines the carbon-emission efficiency of buildings’ heating-related energy consumption in 30 provinces in China from 2005 to 2021 using the super-SBM model. The primary findings of the research are outlined below: (1) During the period from 2005 to 2021, the average carbon-emission efficiency of building heating in 30 provinces in China was 0.789. This figure represents 21.1% of the potential improvement space from the production frontier surface, suggesting significant waste and inefficiency in the current carbon emissions of the construction industry in China. It also indicates that there is room for improvement. (2) There exists a specific correlation between the carbon-emission efficiency of construction and the economic development in each province. The carbon-emission efficiency is higher in the economically developed eastern regions, such as Shanghai, Jiangsu, and Zhejiang. The carbon-emission efficiency is low in the western regions, including Gansu and Ningxia. Enhancing industrial structural adjustment can facilitate the advancement of green and energy-efficient development within the construction industry. (3) The average carbon-emission efficiency in the southern region exceeds that in the northern region. Additionally, the northern region may have a lower overall floor area compared to the southern region. Furthermore, Beijing’s implementation of centralized heating during winter contributes to lower carbon-emission efficiency. The construction of scales can be enhanced, and the energy supply structure can be further modified. (4) The carbon-emission rates of more than one province are higher, and their economic development is better. For example, Hainan and other provinces have experienced rapid development in renewable energy. Additionally, compared to other provinces in the northwest region, Qinghai has achieved good carbon-emission efficiency due to its resource advantages and the development of wind and other energy sources. In the context of China’s dual-carbon goals, there is potential for further advancement in the integrated utilization of renewable energy and the promotion of green practices in buildings’ heating-related energy consumption. There exists a specific correlation between the carbon-emission efficiency of construction and the economic development in each province. The carbon-emission efficiency is higher in the economically developed eastern regions, such as Shanghai, Jiangsu, and Zhejiang. The carbon-emission efficiency is low in the western regions, including Gansu and Ningxia. Enhancing industrial structural adjustments can facilitate the advancement of green and energy-efficient development within the construction industry. The carbon-emission rates of more than one province are higher, and their economic development is better. For example, Hainan and other provinces have experienced rapid development in renewable energy. Additionally, compared to other provinces in the northwest region, Qinghai has achieved good carbon-emission efficiency due to its resource advantages and the development of wind and other energy sources. In the context of China’s dual-carbon goals, there is potential for further advancement in the integrated utilization of renewable energy and the promotion of green practices in buildings’ heating-related energy consumption.

Based on the aforementioned analysis, this paper proposes the following recommendations: Firstly, enhancing the adaptation of the construction industry and fostering the advancement of scale and efficiency. Secondly, it is important to encourage energy transformation, promote the advancement of regional renewable energy, optimize the energy supply structure, and achieve the greening and cleaning of heating. Furthermore, the top-down promotion of energy efficiency in green building, along with a systematic layout, aims to optimize the allocation of resources for green building efficiency. The promotion and implementation of green building policies can promote building transformation to a certain extent. In the future, through top-level design, low-carbon and energy-saving heating in buildings can be standardized from the source, achieving its large-scale application.