Spatiotemporal evolution characteristics of carbon rebound effect in China’s transportation industry and mechanism

Abstract

[Objective] To implement the new development concept and achieve the “dual carbon”, this study reveals the spatiotemporal evolution characteristics of the carbon rebound effect in the transportation industry and its impact mechanism, expands theoretical boundaries of energy rebound research, provides scientific evidence and strategic references for energy-saving and carbon-reduction practices in the transportation sector based on energy structure transformation. [Methods] This study employed spatial kernel density estimation to analyze the spatiotemporal evolution characteristics of the carbon rebound effect in China’s transportation sector from 2009 to 2021. Panel quantile regression was used to investigate the influence of different factors on the carbon rebound effect at various levels, thereby identifying corresponding driving or restraining mechanisms. A geographic detector model was introduced to further refine the influencing mechanisms. [Results] (1) Before the pandemic, the carbon rebound effect in China’s transportation industry primarily followed a pattern of sharp decline followed by gradual rebound, with spatial correlation observed. (2) The carbon rebound effect in the transportation sector were significantly influenced by spatiotemporal factors. Over time, a spatial distribution pattern gradually developed, with weak rebounds predominating and strong rebounds being secondary. (3) The influencing mechanism of the carbon rebound effect in the transportation sector can be divided into four driving or restraining modes (industry-regulation-facility-market driving mode; industry-regulation-energy driving mode; population-finance-government-R&D restraining mode, and population-finance-government restraining mode). Most provinces were affected by low quantile driving and restraining mechanisms, with only a few provinces continuing to be influenced by high quantile restraining and driving mechanisms, primarily located in the Northeast, Northwest, or Southwest regions. (4) Based on the mechanisms influencing carbon rebound effect in the transportation sector, the geographic detector model was introduced to investigate the impact of multi-factor coordination on carbon rebound effect, with the aim of enhancing the cohesion of regional collaborative governance. [Conclusion] The carbon rebound effect is largely homologous to the energy rebound phenomenon. To incorporate the carbon rebound in the transportation industry into policy considerations, and adopt different policy tools based on its temporal and spatial evolution characteristics are essential for accelerating the transformation of transportation energy and adapting to local conditions for emission reduction.

Keywords

• transportation industry|carbon rebound effect|spatial kernel density|quantile regression|geographic detector|china