Spatio-temporal heterogeneity of logistics CO2 emissions and their influencing factors in China: An analysis based on spatial error model and geographically and temporally weighted regression model

Abstract

As one of the ultra-large carbon emitters in China, the logistics industry plays an increasingly essential role in mitigating carbon emissions. Previous studies on logistics CO2 emissions (LCEs) seldom considered the issue of carbon leakage from electricity and the spatio-temporal nonstationarity of factors influencing LCEs. Therefore, using regional grid carbon emission factors, this study calculated the LCEs of 30 Chinese provinces from 2005 to 2019 and, thereafter, employed global and local spatial econometric models – Spatial Error Model (SEM) and Geographically and Temporally Weighted Regression (GTWR) model, respectively – to uncover the spatio-temporal heterogeneous impact of influencing factors on LCEs. The results suggest that: (1) The overall LCEs continue to rise, presenting significant spatial difference with an increasing gradient pattern of the “Western-Central-Eastern.” (2) The LCEs show a spatial agglomeration, with high–high (H–H) and low–low (L–L) clusters as the main types. (3) Economic development level, energy intensity, and electricity consumption significantly promote the LCEs, and the intensity of the promotion presenting a downward, steady, and upward trend, respectively. (4) Population urbanization inhibits LCEs in north China, but boosts them in south China; the inhibition effect of industrial structure on LCEs, which is mainly pronounced in the provinces along the Yangtze River Economic Belt, tends to transform into a promoting effect.