Evaluating Carbon-Emission Efficiency in China’s Construction Industry: An SBM-Model Analysis of Interprovincial Building Heating
Abstract
:1. Introduction
2. Literature Review
3. Research Methods and Data Selection
3.1. Research Methodology
3.2. Tobit Regression Model
3.3. Indicator Selection
4. Results and Discussion
4.1. DEA Result
4.2. Influence Factors of Carbon-Emission Efficiency Based on the Tobit Regression
- (1)
- Science and technology levels:
- (2)
- Regional economic scale:
- (3)
- Government intervention:
- (4)
- Industrial structure:
4.3. Unit Root Test for Panel Data
4.4. Tobit Regression Results
5. Conclusions and Recommendations
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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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). |
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 |
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 ↑ |
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 |
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] |
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 *** |
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 |
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Yuan, R.; Xu, X.; Wang, Y.; Lu, J.; Long, Y. Evaluating Carbon-Emission Efficiency in China’s Construction Industry: An SBM-Model Analysis of Interprovincial Building Heating. Sustainability 2024, 16, 2411. https://doi.org/10.3390/su16062411
Yuan R, Xu X, Wang Y, Lu J, Long Y. Evaluating Carbon-Emission Efficiency in China’s Construction Industry: An SBM-Model Analysis of Interprovincial Building Heating. Sustainability. 2024; 16(6):2411. https://doi.org/10.3390/su16062411
Chicago/Turabian StyleYuan, Ruiqing, Xiangyang Xu, Yanli Wang, Jiayi Lu, and Ying Long. 2024. "Evaluating Carbon-Emission Efficiency in China’s Construction Industry: An SBM-Model Analysis of Interprovincial Building Heating" Sustainability 16, no. 6: 2411. https://doi.org/10.3390/su16062411