Increased global carbon dioxide emissions have become a global priority due to their contribution to the greenhouse effect and climate change.The Chinese government has responded by pledging to achieve carbon dioxide peaking by 2030 as part of its concept of green development,which calls for the efforts of all spheres of life.Due to rapid economic growth and infrastructure improvements,the logistics industry,as a vital industrial sector in the national economy,has seen significant increases in consumption of energy and carbon emissions.This poses a substantial challenge to China’s commitment to achieving carbon peak before 2030.Despite extensive research by scholars on energy consumption,carbon emissions,and other factors influencing the logistics industry,there are still pressing issues that must be addressed.Due to these problems,it is challenging for the Chinese government and businesses to accurately manage energy conservation and emission reduction,which could limit or make it impossible to meet future emission reduction targets.Therefore,it is crucial to address these challenges to achieve China’s carbon dioxide peaking by 2030,and this paper provides insights into the logistics industry’s carbon emissions,challenges,and opportunities for reducing emissions.This paper first employs the "top-down" approach to calculate the logistics industry’s use of energy and carbon emissions from 2000 to 2020,utilizing the most recent carbon emission correlation released by the United Nations International Panel on Climate Change.The emissions are then measured and examined.The generalized divisia index method,which was developed by expanding the Kaya identity,is then used to measure the contribution percentage and contribution value of each influential factor to the transition in carbon dioxide emissions in the logistics industry.The results of the staged and cumulative contributions are compared to identify the key factors influencing carbon dioxide emissions in the logistics industry.Based on the Kaya identity,a carbon emission prediction model is developed.Finally,combining Monte Carlo simulation with dynamic scenario analysis,the baseline scenario,low-carbon scenario,and technological breakthrough scenario are determined.To simulate future changes and the peak status of carbon emissions in the logistics industry,the average annual potential change rate of the factors influencing carbon emissions in the industry is calculated under each scenario.The study findings demonstrate that:(1)Over the 2000-2020 period,the logistics industry’s carbon emissions increased steadily,with a total of 169.31 million tons emitted in 2020.(2)The added value of the logistics industry emerged as the most significant driver of carbon emissions(contributing66.73%),while the output carbon intensity was identified as the primary factor in reducing carbon emissions(contributing-26.59%).(3)According to the findings of 500,000 dynamic simulations,the three scenarios result in the logistics sector’s carbon emissions following a normal distribution.The baseline scenario predicts that there won’t be a peak before 2030;the low-carbon scenario predicts that there’s a small chance that carbon emissions will start to decline between 2026 and 2030;and the technological breakthrough scenario predicts that the peak will be reached in 2025 with a peak of 175.27 million tons.This study advances the theoretical foundation and evidence-based methodologies for assessing the variables that affect carbon emissions in the logistics industry.The findings of this study can be used to evaluate the history carbon emissions of the logistics sector and to analyze potential carbon peak scenarios.On a practical level,the research outcomes can assist both the government and private sector organizations in implementing effective measures to reduce carbon emissions.Additionally,this study provides significant guidance for developing a comprehensive carbon peaking plan for the logistics industry. |