ORIGINAL RESEARCH
Analysis of the Spatio-Temporal Characteristics
and Influencing Factors of Carbon Emissions
in the Chinese Building Sector
More details
Hide details
1
Qingdao University of Technology, Shandong, Qingdao, 266520, China
Submission date: 2022-10-06
Final revision date: 2023-02-25
Acceptance date: 2023-03-04
Online publication date: 2023-05-04
Publication date: 2023-06-23
Pol. J. Environ. Stud. 2023;32(4):3355-3372
KEYWORDS
TOPICS
ABSTRACT
Firstly, the spatial and temporal characteristics of carbon emissions in the Chinese building sector
are explored. Secondly, the spatial and temporal heterogeneity of the provincial contributions to carbon
emissions in the Chinese building sector and their influencing factors are analyzed. Finally, the spatial
and temporal evolution of carbon emissions in the Chinese building sector is projected for the period
2022-2050. The results show that: (1) From 2010 to 2021, carbon emissions in the Chinese buildings
still maintain a high growth trend. In addition, building carbon emissions also offer unbalanced
features in space. (2) The level of spatial aggregation of carbon emissions from buildings in China is on
the rise. (3) The intensity of economic activity and per capita floor area are the main factors driving
the increase in carbon emissions in the Chinese buildings, and energy consumption per unit of GDP
is the most significant factor mitigating the rise in carbon emissions in the Chinese buildings. The level
of contribution of each element to the carbon emissions in the Chinese buildings varies considerably
from province to province. (4) Carbon emissions in Chinese building sector will peak at 2.463 BtCO2
in 2036.
CONFLICT OF INTEREST
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
CITATIONS (8):
1.
Dynamic Analysis of China’s Urban Economic Spatial Network and Its Multidimensional Impact on Building Carbon Emissions
Juan Li, Mei Sun
Mathematics
2.
Deep learning and dynamic simulation based progressive decarbonization roadmap in the building sector in Jiangsu Province
Shouxin Zhang, Meiping Wang, Dongzhi Guan, Zhuoshi Shen, Yebin Yu
Sustainable Cities and Society
3.
Impact factors and peaking simulation of carbon emissions in the building sector in Shandong Province
Shouxin Zhang, Meiping Wang, Haiyong Zhu, Huanzhi Jiang, Jiazhen Liu
Journal of Building Engineering
4.
Deep learning-based stepwise peaking roadmap of carbon emissions in Chinese provincial building sector
Shouxin Zhang, Meiping Wang, Huanzhi Jiang, Dongzhi Guan
Building and Environment
5.
Cyber Security Intelligence and Analytics
Zi Wang, Jiang Yu
6.
Deep Learning-Based Study of Carbon Emissions Peak Pathways in Chinese Building Sector: Incorporating Legal and Policy Text Quantification
Zhixuan Dai, Shouxin Zhang, Dongzhi Guan
Sustainability
7.
Spatiotemporal evolution and regional heterogeneity of carbon emissions in municipal-level building sector in China
Meiping Wang, Shouxin Zhang, Jin Shao, Quan Wen, Jingke Hong, Xiangyang Tao
Building Simulation
8.
Deep learning-based long-medium prediction of Chinese new energy vehicle sales and air quality towards 2035
Meiping Wang, Jin Shao, Shouxin Zhang, Jingke Hong, Xiangyang Tao, Li Guo
Transportation Research Part D: Transport and Environment