Decoupling effect and spatial-temporal characteristics of carbon emissions from construction industry in China

The construction industry is one of the most energy-intensive industries in China, which contributes to high carbon emissions and hinders efforts to reduce the emissions. The high energy consumption and emission have seriously constrained the growth of low carbon economy in China's construction industry. And reducing the carbon emission from construction industry (CECI) has become the key to sustainable development and the achievement of double carbon target. The paper firstly decomposes the main influencing factors and calculates their effects by LMDI model. Then, the Tapio decoupling index is used to explore the decoupling relationship between carbon emissions and economic output. Finally, the spatial-temporal evolution of carbon emissions is analyzed through spatial autocorrelation theory. The results show that the carbon emissions from construction industry are highly increased by the construction employment rate (EM), and may be reduced by the energy intensity (EI) and carbon emission efficiency (CI). And the regions with better decoupling status are the eastern and central regions, which have relatively developed construction industry. Moreover, the spatial-temporal characteristic of carbon emissions is high in the east and low in the west, high in the south and low in the north, showing obviously regional differences.

Automated summary

The construction industry in China is highly energy-intensive and contributes to high carbon emissions, hindering efforts to reduce emissions. This study focuses on reducing carbon emissions from the construction industry (CECI) in order to achieve sustainable development and double carbon target. By using the LMDI model, the study identifies the main influencing factors and their effects, and explores the decoupling relationship between carbon emissions and economic output using the Tapio decoupling index. The spatial-temporal evolution of carbon emissions is also analyzed, revealing regional differences.