Spatiotemporal Analysis of Influencing Factors of Carbon Emission in Public Buildings in China
Author(s): |
Zhuoqun Du
Yisheng Liu Zhidong Zhang |
---|---|
Medium: | journal article |
Language(s): | English |
Published in: | Buildings, 11 April 2022, n. 4, v. 12 |
Page(s): | 424 |
DOI: | 10.3390/buildings12040424 |
Abstract: |
The rapid development of public buildings has greatly increased the country’s energy consumption and carbon emissions. Excessive carbon emissions contribute to global warming. This paper aims to measure the carbon emissions in the operation of public buildings, and to identify the multiple influencing factors of carbon emissions in operational public buildings. First, the spatial and temporal variation characteristics of carbon emissions from public buildings in 30 provinces of China from 2008–2019 are analyzed. Second, a green building index is constructed, and the STIRPAT (Stochastic Impacts by Regression on Population, Affluence, and Technology) model is utilized to explore the relationship between each influencing factor and carbon emissions, using spatial and temporal geographically weighted regression analysis. The results show that the effects of population, urbanization rate, GDP per capita, green building index, and industrial structure on carbon emissions from public buildings all show spatial correlation and differences. There are east-west differences in the operational carbon emissions of public buildings in China’s provinces. Cluster evolution shows a spatially increasing trend from west to east. To some extent, policymakers can develop appropriate policies for different provinces through the findings. |
Copyright: | © 2022 by the authors; licensee MDPI, Basel, Switzerland. |
License: | This creative work has been published under the Creative Commons Attribution 4.0 International (CC-BY 4.0) license which allows copying, and redistribution as well as adaptation of the original work provided appropriate credit is given to the original author and the conditions of the license are met. |
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data sheet - Reference-ID
10664243 - Published on:
09/05/2022 - Last updated on:
01/06/2022