Logistics standardization is the formulation and implementation of uniform standards and norms, significantly reducing logistics costs and improving logistics efficiency. Therefore, logistics standardization is conducive to promoting the spatial reallocation of economic activities and unimpeded national economic cycles, and provides a new opportunity for pollution and carbon reduction.

In this paper, the logistics standardization pilot work gradually carried out in various cities is regarded as a quasi-natural experiment, and a double machine learning model is used to examine the impact of logistics standardization on urban pollution and carbon reduction. It is found that logistics standardization plays a role in reducing pollution and carbon. Mechanism testing shows that this effect is mainly achieved by promoting biased structural upgrading of the service industry, stimulating green innovation, and forming economic agglomeration. Heterogeneity analysis shows that this effect varies significantly under different scenarios of logistics costs, digital-real integration, and regulatory pressure, and is stronger in regions with high levels of transportation infrastructure, digital infrastructure, and environmental regulation intensity. Further research finds that logistics standardization effectively enhances the synergy between pollution reduction and carbon reduction. In addition, the improvement of logistics standardization construction quality is more conducive to promoting urban pollution and carbon reduction.

The marginal contributions of this paper are as follows: First, at the research entry point level, based on the unique background of logistics standardization construction in China, logistics standardization and pollution and carbon reduction are simultaneously included in the same research framework for the first time, providing new ideas and solutions for improving the ecological environment. Second, at the methodology level, double machine learning is applied to the assessment of the pollution and carbon reduction effect of logistics standardization, taking advantage of machine learning’s handling of high-dimensional data and complex variable relationships to improve the precision of causal effect estimation. Third, at the research content level, the theoretical black box of logistics standardization affecting pollution and carbon reduction is revealed, and its heterogeneous performance is examined from multiple dimensions.