Analysis of the spatial relevance and influencing factors of carbon emissions in the logistics industry from China

• Published in: Environmental science and pollution research international Vol. 29; no. 2; pp. 2672 - 2684
• Main Authors: Guo, Xiaopeng, Wang, Dandan
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.01.2022; Springer Nature B.V
• Subjects: Agglomeration; Aquatic Pollution; Atmospheric Protection/Air Quality Control/Air Pollution; Carbon; Carbon - analysis; Carbon Dioxide - analysis; Carbon neutrality; China; Earth and Environmental Science; Economic Development; Ecotoxicology; Emissions; Emissions control; Energy; Environment; Environmental Chemistry; Environmental Health; Environmental science; freight; Impact analysis; Industry; infrastructure; Logistics; Policies; Population; Professional development; Research Article; Spatial analysis; Technological change; Urban areas; Urban Renewal; Waste Water Technology; Water Management; Water Pollution Control; China; Spatial lag model; Spatial effect; Energy; STIRPAT; Carbon emissions; Logistics
• ISSN: 0944-1344; 1614-7499; 1614-7499
• DOI: 10.1007/s11356-021-15742-0

This study attempts to analyze the impact of population, property, technology, energy factors, and spatial agglomeration in the logistics industry on carbon emissions. To achieve the goal of peak carbon and carbon neutrality, the relationship between influencing factors and carbon emissions was analyzed based on panel data from the logistics industry for 30 provinces in China from 2003 to 2017 using an improved STIRPAT (Stochastic Impacts by Regression on Population, Affluence, and Technology) model and a spatial lag model (SLM). The results show that population, property, technology, and energy factors in the logistics industry all have different degrees of influence on carbon emissions, wherein population, energy, and property have a greater influence, which implies that carbon emission reduction policies can be carried out considering the relevant aspects. In addition, under the influence of spatial agglomeration, the degree of influence of freight mileage (FM), total fixed-asset investment (TFAI), and industry population (IPOP) on carbon emissions decreases, and the degree of influence of energy intensity (EI) and industry per capita GDP (IPCG) increases. This suggests that corresponding emission reduction policies should be formulated for large urban areas based on technological innovation, infrastructure, and talent training, while smaller urban areas can focus on developing new energy and industrial economies. These findings help to complement the existing literature and provide policymakers with some insights related to urban logistics development.