Decoupling relationship between logistics growth and carbon emissions and driving factors in Chongqing: A novel decomposition framework

Abstract

A comprehensive understanding of the logistics decoupling status and driving factors is of immense theoretical and practical importance for rationally formulating low-carbon logistics policies and accelerating the realization of high-quality development. Present study introduces the logarithmic mean Divisia index method (LMDI) decomposition technique into Tapio index model, extends traditional decoupling model, and establishes a new analytical framework for the decoupling relationship between logistics growth and carbon emissions. Using long-term data from Chongqing (1997–2021), this study investigated the Chongqing logistics decoupling relationship and driving factors. The study found the following: (1) Chongqing logistics carbon emissions show phased changes and face greater pressure to reduce emissions. (2) The decoupling status of logistics growth and carbon emissions is predominantly expansive and weak decoupling, with an overall evolutionary trend of “expansive decoupling–weak decoupling–strong decoupling. The average decoupling index in 2013–2021 was 0.5523, indicating a decreasing trend; however, there was still a large gap in realizing a strong decoupling goal. (3) The energy consumption intensity effect facilitates logistics carbon decoupling, the economic scale effect has a strong inhibiting effect, and the industrial structure effect and transportation intensity effect are staged. Finally, targeted policy recommendations are proposed to expedite logistics carbon emissions decoupling in Chongqing.