Influencing factors, scenario prediction and decoupling effect of carbon intensity of the whole process of agricultural products logistics

Fan, Xiangpeng; G, Yao; Zhang, Dongmei; Bian, Xiaoyu

doi:10.21203/rs.3.rs-722065/v1

Cited by 1 publication

(1 citation statement)

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“…Through data simulations, they validated the interaction mechanisms involved in the evolution of a low‐carbon rural logistics system in China. Most past research focused on low‐carbon economies and agricultural logistics (Fan et al, 2021; Li & Chen, 2018). Scholars have conducted empirical studies on the coupling mechanisms between agricultural logistics and low‐carbon economies.…”

Section: Literature Reviewmentioning

confidence: 99%

Towards low‐carbon transition: Coordinating development and decarbonisation in rural logistics

Bian

2023

Syst Res Behav Sci

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Over the past decade, there is a growing global call for low carbon development. However, there is no adequate low‐carbon policies to address the trade‐off between rural logistics development and low‐carbon goals. Jiangsu is one of the pilot sites for most of China's policy reforms and has the potential to develop a low carbon transformation of rural logistics. Therefore, we provide an in‐depth analysis of the above trade‐offs from an industrial perspective by integrating the key features of rural logistics into a unified framework and modelling a rural logistics system dynamic model using rural logistics in Jiangsu as a case study. We compare the development trend of Jiangsu's rural logistics industry from 2021 to 2030 under the traditional development model, the green image policy, the fixed carbon policy and the fixed carbon tax rebate policy. The findings show that, first, in the absence of policy intervention, rural logistics in Jiangsu will not actively adopt any low‐carbon behaviour, which will lead to an inevitable rise in CO2. Second, the simulation results in fixed carbon policy show that the policies that impose excessive burdens on rural logistics are invalid rural logistics that need enough funds to develop their own supply capacity and carbon abatement capacity. Third, the green image policy is more advantageous in addressing the trade‐offs between rural logistics development and the low‐carbon transition, which not only avoids burdening rural logistics but also improves the supply capacity of rural logistics by increasing the demand for rural logistics. Finally, the government can implement the green image policy in the early stage and then introduce the carbon tax rebate policy in the middle stage to improve and increase the capacity of the rural logistics sector to embrace low carbon transition, which can help implement subsequently implement the carbon tax rebate policy.

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Section: Literature Reviewmentioning

confidence: 99%