The fast-paced growth of the logistics industry has contributed significantly to China’s high-quality economic development. However, the growth of the logistics industry is frequently accompanied by high levels of pollution, carbon emissions, and energy consumption. How to increase low-carbon logistics capacity has emerged as a research hotspot under the dual carbon goals. This study used entropy weight TOPSIS to evaluate the low-carbon logistics capacity and dynamic fuzzy-set qualitative comparative analysis (fsQCA) to shed light on the antecedent conditions that influenced low-carbon logistics capability by using panel data from 30 Chinese provinces between 2008 and 2021. Based on the empirical results, several conclusions are drawn: (1) The comprehensive score show that while province low-carbon logistics capacity varies, most of them exhibit a general growing tendency in most provinces, where Beijing, Shanghai, Tianjin, Guangdong and Zhejiang rank among the top five. (2) Three configurations, digital empower capital intensive type, digital empower labor intensive type, and green ecology plus technology innovation type that lead to better low-carbon logistics capacity. Related policy recommendations are proposed, including strengthening the synergistic development of the digital economy and the logistics industry, promoting research and development of green and innovative technologies, and reinforcing the constraints of the dual-carbon target.

Green innovation is a critical support to combat climate change arising from greenhouse gas emissions generated by energy consumption. It is an essential way to achieve resource storage, carbon emissions reduction, and sustainable development goals in China. Based on an environmental framework defined as the Stochastic Impacts by Regression on Population, Affluence, and Technology (STIRPAT) model, this study aimed to empirically check the impact of green innovation (GI), per capita GDP (PGDP), population density (PD), environmental regulations (ER), energy consumption (EC), and industrial structure upgrading (ISU) on CO₂ emissions (CO_2e). For this purpose, a sample dataset covering the 30 provincial regions in mainland China from 2005 to 2019 was analyzed using the Fixed Effects and System Generalized Method of Moment (SYS-GMM) Methodology. The empirical results showed that CO_2e in the current period were further aggravated due to the agglomeration effect of CO_2e from the previous period. The data analysis indicated that GI, ER, and ISU all exert a significant inhibitory effect on CO_2e, whereas PGDP, PD, and EC had a positive effect on carbon emissions when dynamic relationships were analyzed. In the regional heterogeneity test, the current model also revealed that the impact of GI on diminishing CO_2e was more pronounced in the east-central region, but not in the west. It is suggested that policymakers in China not only design differentiated policies in response to regional heterogeneity, but also focus on the decisive role of green technology application, environmental protection, and green transformation of industrial structure in curbing CO_2e.