Spatial differences, dynamic evolution and influencing factors of China's construction industry carbon emission efficiency

Improving the construction industry carbon emission efficiency (CICEE) is crucial for achieving sustainable development. To promote low-carbon development in the construction industry, it is essential to measure carbon emission efficiency (CEE) and analyze spatial differences, dynamic evolution, and influencing factors. This study measures CICEE in 30 provinces in China from 2005 to 2019 and evaluates CEE using the minimum distance to a strong efficient frontier (MinDS) model with undesirable outputs. Subsequently, the Dagum Gini coefficient and its decomposition, as well as spatial autocorrelation analysis, are used to explore the sources of spatial differences and the spatial clustering pattern of CEE. The dynamic trend of CEE is analyzed through kernel density estimation, traditional and spatial Markov chains. Finally, geographical detectors are used to detect the explanatory factors and their interactions on spatial differences in CEE. The results of this study show that the CICEE presents an increasing and then decreasing trend, with the highest CEE in the eastern region, followed by the central and northeastern regions, and the lowest in the western region. Additionally, the eastern region exhibits the highest intra-regional differences and the highest inter-regional differences with the western region. Meanwhile, CEE shows a positive spatial correlation, with high-high (H-H) clustering in the eastern region and low-low (L-L) clustering in the western and northeastern regions. Polarization has been evident throughout the entire country and its four regions in recent years. It is challenging to achieve the CEE transfer through rapid advancement, and the efficiency of neighboring provinces will influence the potential transfer of the local province. Finally, factors such as enterprise scale, economic development level, degree of openness to the outside world, innovation level, industrial structure, and energy consumption structure all affect the spatial differences in CEE, with the interaction effect being higher than the single factor. This study presents a novel computational model to measure CICEE, analyzes the structural factors contributing to the spatial differences in CICEE, and provides theoretical support for the synergistic improvement of CEE across different regions. Combining with spatial autocorrelation analysis, the spatial distribution characteristics of CICEE are analyzed from the static level. This study provides a comprehensive examin