Drivers of Carbon Emissions in China’s Construction Industry: A Perspective from Interregional Carbon Transfer
Abstract
1. Introduction
1.1. Research Status of Construction Carbon Emissions
1.2. Research Status on Carbon Transfer
1.3. Research Status on Factors Influencing Construction Carbon Emissions
1.4. Review of Research
2. Materials and Methods
2.1. InterRegional Carbon Emission Transfer Model for the Construction Industry
2.1.1. Carbon Emission Measurement Model of the Construction Industry
2.1.2. Interregional Construction Carbon Emission Transfer Measurement Model
- (1)
- Calculating Construction Carbon Emission Transfer Coefficients
- (2)
- Calculate the total amount of carbon transfer in the construction industry.
2.2. Carbon Emission Transfer Network Model of the National Construction Industry
2.3. Model of Carbon Emission Drivers in the Construction Industry
2.3.1. Construction of Panel Regression Model
2.3.2. The Selection of Dependent and Independent Variables
- Construction Carbon Emission Inflow and Outflow Intensity (INE, OUTE):
- Mediation Capacity (BC):
- Degree of Influence (EC):
2.3.3. The Selection of Control Variables
2.4. Data Sources and Process
3. Measurement of Interregional Construction Carbon Emission Transfers
3.1. Analysis of Interregional Total Construction Carbon Emissions
3.2. Analysis of Interregional Construction Carbon Emission Transfer
4. Results
4.1. Analysis of China’s Construction Carbon Emission Transfer Network
4.1.1. Construction of the Complex Network for China’s Construction Carbon Emission Transfers
4.1.2. Analysis of Construction Carbon Emission Transfer Network Metrics
- Overall Network Characteristics
- Individual Network Characteristics
- Strength
- 2.
- Mediation Capacity
- 3.
- Degree of Influence
4.2. Analysis of Drivers of Construction Carbon Emissions
4.2.1. Benchmark Model Selection
4.2.2. Multicollinearity Test
4.2.3. Heteroscedasticity Test
4.2.4. Panel Regression Results
4.2.5. Robustness Test
4.2.6. Endogeneity Test
5. Discussion
6. Conclusions and Recommendations
6.1. Conclusions
6.2. Recommendations
Supplementary Materials
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
Abbreviations
DDZ | Demand-Driven Zones |
BDZ | Balanced Development Zones |
SSZ | Specialized Supplementary Zones |
MDZ | Major Development Zones |
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Energy | Coal | Crude Oil | Petroleum | Coke | Petrol | Diesel | Diesel Oil | Fuel Oil |
---|---|---|---|---|---|---|---|---|
Carbon emission factor | 2.69 Kg CO2/Kg | 2.76 Kg CO2/L | 2.09 Kg CO2/m3 | 3.14 Kg CO2/Kg | 3.14 Kg CO2/L | 2.56 Kg CO2/L | 2.73 Kg CO2/L | 3.14 Kg CO2/L |
Building Material | Clinker | Nylon | Steels | Aluminum | Lumber |
---|---|---|---|---|---|
Carbon emission factors | 0.815 kg/kg | 0.9655 kg/kg | 1.789 kg/kg | 2.6 kg/kg | −842.8 kg/m3 |
Recovery factor | — | 0.7 | 0.8 | 0.85 | 0.2 |
Abbreviations of Province | Province | Abbreviations of Provinces | Province |
---|---|---|---|
BJ | Beijing | QH | Qinghai |
TJ | Tianjin | NX | Ningxia |
HE | Hebei | NM | Inner Mongolia |
SX | Shanxi | LN | Liaoning |
SH | Shanghai | JL | Jilin |
JS | Jiangsu | HL | Heilongjiang |
ZJ | Zhejiang | AH | Anhui |
FJ | Fujian | SD | Shandong |
JX | Jiangxi | SC | Sichuan |
HA | Henan | SN | Shaanxi |
HB | Hubei | GS | Gansu |
GX | Guangxi | XJ | Xinjiang |
HI | Hainan | GD | Guangdong |
CQ | Chongqing | YN | Yunnan |
GZ | Guizhou | HN | Hunan |
Year | 2007 | 2010 | 2012 | 2015 | 2017 |
---|---|---|---|---|---|
Node | 30 | 30 | 30 | 30 | 30 |
Edge | 399 | 414 | 283 | 382 | 382 |
Density | 0.459 | 0.436 | 0.325 | 0.439 | 0.441 |
Average path length | 1.505 | 1.417 | 1.684 | 1.423 | 1.444 |
Dependent Variable | F-Test | Hausman-Test | ||
---|---|---|---|---|
CDCE | F-statistic | p-value | p-value | |
5.61 | Prob > F = 0.0000 | 17.77 | Prob > F = 0.0068 |
Variable | VIF | 1/VIF |
---|---|---|
CCL | 3.350 | 0.299 |
UL | 2.720 | 0.367 |
CE | 2.500 | 0.401 |
EC | 1.440 | 0.692 |
BC | 1.350 | 0.742 |
OUTE | 1.310 | 0.765 |
MES | 1.310 | 0.766 |
SP | 1.280 | 0.778 |
INE | 1.030 | 0.967 |
Mean VIF | 1.810 |
BP Test Statistic | p-Value |
---|---|
43.35 | 0.0000 |
Variables Type | Variables Name | Variable | Unit | Mean | Standard Deviation | Maximum | Minimum |
---|---|---|---|---|---|---|---|
Dependent Variable | Construction Carbon Emissions | CDCE | t | 7.38 × 107 | 8.81 × 107 | 4.46 × 108 | 1.39 × 106 |
Independent Variables | Carbon Emission Inflow Intensity | INE | t | 16,843.352 | 37,498.199 | 2.15 × 105 | 39.394 |
Carbon Emission Outflow Intensity | OUTE | t | 19,847.692 | 95,051.726 | 8.81 × 105 | 0.000 | |
Mediation Capacity | BC | - | 10.693 | 21.956 | 115.985 | 0.000 | |
Degree of Influence | EC | - | 0.625 | 0.238 | 1.000 | 0.074 | |
Control Variables | Population Size | SP | capita | 4505.100 | 2752.086 | 12,141.000 | 552.000 |
Urbanization Rate | UL | % | 0.544 | 0.136 | 0.916 | 0.282 | |
Construction Economic Output | CCL | CNY/capita | 1.015 | 0.898 | 4.438 | 0.096 | |
Per Capita Net Income in Construction | CE | 37,814.173 | 16,392.943 | 99,718.000 | 13,102.000 | ||
Machinery/Equipment Intensity | MES | Kw/m2 | 0.091 | 0.04 | 0.546 | 0.018 |
Variable | Model(1) | Model(2) | Model(3) | Model(4) | Model(5) |
---|---|---|---|---|---|
CDCE | CDCE | CDCE | CDCE | CDCE | |
SP | 42,783.230 *** (3.089) | 41,510.669 *** (3.007) | 36,567.397 *** (3.327) | 42,358.354 *** (2.933) | 42,746.289 *** (3.022) |
UL | 3.93 × 108 ** (2.378) | 3.76 × 108 ** (2.282) | 3.69 × 108 ** (2.749) | 3.91 × 108 ** (2.270) | 3.93 × 108 ** (2.340) |
CCL | 6.59 × 107 *** (3.815) | 6.55 × 107 *** (3.688) | 4.98 × 107 *** (3.376) | 6.58 × 107 *** (3.750) | 6.60 × 107 *** (3.884) |
CE | −1504.405 ** (−2.102) | −1487.036 * (−2.040) | −1076.856 * (−1.929) | −1483.795 * (−1.936) | −1504.980 ** (−2.110) |
MES | 5.77 × 108 ** (2.323) | 5.42 × 108 ** (2.093) | 4.27 × 108 * (1.966) | 5.81 × 108 ** (2.337) | 5.79 × 108 ** (2.346) |
INE | 87.307 (1.214) | ||||
OUTE | 246.655 *** (7.391) | ||||
BC | 32,452.000 (0.227) | ||||
EC | 1.45 × 106 (0.061) | ||||
_cons | −3.47 × 108 *** (−2.965) | −3.34 × 108 *** (−2.866) | −3.09 × 108 *** (−3.330) | −3.45 × 108 *** (−2.810) | −3.48 × 108 *** (−3.070) |
R2 | 0.498 | 0.501 | 0.639 | 0.498 | 0.498 |
N | 150 | 150 | 150 | 150 | 150 |
Variable | Model (1) CDCE | Model (2) CDCE | Model (3) CDCE | Model (4) CDCE | Model (5) CDCE |
---|---|---|---|---|---|
SP | 39,511.201 *** (3.269) | 38,098.861 *** (3.139) | 33,650.401 *** (3.321) | 40,060.276 *** (3.127) | 39,690.650 *** (3.165) |
UL | 4.73 × 108 *** (2.959) | 4.62 × 108 *** (2.886) | 4.15 × 108 *** (3.235) | 4.77 × 108 *** (2.828) | 4.75 × 108 *** (2.881) |
CCL | 6.47 × 107 *** (4.001) | 6.40 × 107 *** (3.835) | 5.08 × 107 *** (3.640) | 6.50 × 107 *** (3.847) | 6.44 × 107 *** (4.019) |
CE | −1827.381 ** (−2.424) | −1800.270 ** (−2.354) | −1308.195 ** (−2.304) | −1859.672 ** (−2.239) | −1826.209 ** (−2.421) |
MES | 4.06 × 108 (1.160) | 4.23 × 108 (1.269) | 3.51 × 108 (1.106) | 3.93 × 108 (1.089) | 4.07 × 108 (1.144) |
INE | 93.975 (1.257) | ||||
OUTE | 241.448 *** (7.429) | ||||
BC | −3.72 × 104 (−0.215) | ||||
EC | −4.97 × 106 (−0.205) | ||||
_cons | −3.59 × 108 *** (−3.305) | −3.49 × 108 *** (−3.206) | −3.12 × 108 *** (−3.597) | −3.62 × 108 *** (−3.149) | −3.57 × 108 *** (−3.378) |
R2 | 0.486 | 0.489 | 0.622 | 0.486 | 0.486 |
N | 120 | 120 | 120 | 120 | 120 |
Variable | Model(1) |
---|---|
CDCE | |
SP | 33,571.057 *** (3.055) |
UL | 3.40 × 108 ** (2.455) |
CCL | 4.89 × 107 *** (3.258) |
CE | −990.928 * (−1.705) |
MES | 4.07 × 108 * (1.777) |
INE | 100.186 (1.521) |
OUTE | 250.229 *** (7.296) |
BC | 98,119.868 (0.817) |
EC | 6.33 × 106 (0.285) |
_cons | −2.89 × 108 *** (−3.151) |
R2 | 0.645 |
N | 150 |
Variables | (1) |
---|---|
GMM | |
L.CDCE | −0.206 |
(0.177) | |
INE | −68.240 |
(268.431) | |
OUTE | 217.470 *** |
(58.461) | |
BC | 874,647.945 |
(663,335.705) | |
EC | −5.599 × 107 |
(51,537,095.262) | |
SP | 25,445.321 |
(46,455.130) | |
UL | 2.347 × 108 |
(6.652 × 108) | |
CCL | 71,846,623.447 ** |
(30,406,021.534) | |
CE | −241.031 |
(3632.386) | |
MES | 9.972 × 108 |
(4.658 × 109) | |
N | 120 |
AR (1) | 0.0545 |
AR (2) | 0.183 |
Hansen | 0.190 |
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Share and Cite
Xiao, W.; Song, W.; Pei, X.; Wang, L. Drivers of Carbon Emissions in China’s Construction Industry: A Perspective from Interregional Carbon Transfer. Buildings 2025, 15, 1667. https://doi.org/10.3390/buildings15101667
Xiao W, Song W, Pei X, Wang L. Drivers of Carbon Emissions in China’s Construction Industry: A Perspective from Interregional Carbon Transfer. Buildings. 2025; 15(10):1667. https://doi.org/10.3390/buildings15101667
Chicago/Turabian StyleXiao, Wenwen, Wenhao Song, Xuemei Pei, and Lili Wang. 2025. "Drivers of Carbon Emissions in China’s Construction Industry: A Perspective from Interregional Carbon Transfer" Buildings 15, no. 10: 1667. https://doi.org/10.3390/buildings15101667
APA StyleXiao, W., Song, W., Pei, X., & Wang, L. (2025). Drivers of Carbon Emissions in China’s Construction Industry: A Perspective from Interregional Carbon Transfer. Buildings, 15(10), 1667. https://doi.org/10.3390/buildings15101667