Spatio-temporal patterns and impact mechanisms of CO2 emissions from China's construction industry under urbanization

Abstract

The construction industry fuels China's rapid urbanization, which also escalates carbon emissions. This study aims to investigate the spatio-temporal patterns and impact mechanisms of construction carbon emissions (CCE) under urbanization to inform low-carbon policies. We investigate the spatial heterogeneity and temporal trends of China's CCE employing the carbon emission coefficient method and the backpropagation neural network, identify various development patterns of CCE under urbanization using the K-means clustering method, and further comprehensively explore the key influencing factors through the logarithmic mean Divisia index decomposition method. Our findings indicate that: (1) CCE presents a notable spatio-temporal polarization and aggregation effect during 2009–2018 in China. Compared with rough development scenario, the green development scenario is predicted to effectively reduce CCE by 5.17 % in 2030. (2) The relationship between urbanization and CCE is revealed to be an “inverted U-shape” and four different development patterns are identified. (3) Overall, the output intensity and structural effect of fixed asset inputs are critical factors contributing to CCE. Importantly, the impact mechanisms of CCE vary under different development patterns. This study provides essential evidence-based information and scientific support for CCE reduction in China, which highlights targeted and tailored strategies for reducing CCE considering different patterns under urbanization.