2021
DOI: 10.15244/pjoes/130334
|View full text |Cite
|
Sign up to set email alerts
|

Research on Measurement of Regional Differences and Decomposition of Influencing Factors of Carbon Emissions of China’s Logistics Industry

Abstract: Based on the energy consumption data of the logistics industry in 30 provinces and cities in China, this paper uses the carbon emission accounting method of IPCC to estimate the total carbon emissions of the logistics industry in China from 2010 to 2019, and introduces the carbon emission Theil index. The Logarithmic Mean Divisia Index (LMDI) model is used to measure the regional differences in carbon emissions of China's logistics industry and decompose the influencing factors. The research results show that … Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
1
1
1
1

Citation Types

1
10
0

Year Published

2021
2021
2024
2024

Publication Types

Select...
9
1

Relationship

0
10

Authors

Journals

citations
Cited by 22 publications
(11 citation statements)
references
References 10 publications
1
10
0
Order By: Relevance
“…From 1995 to 2007, Li and Li (2010) [40] argued that the overall carbon emissions of all regions rose annually, which is consistent with this paper's finding that year is positively connected with carbon emissions. Using the STIRPAT model and panel data methodologies, they also analysed the impact of population, economics, and technology on regional carbon emissions, concluding that rapid economic growth is the most significant factor in the increase of carbon emissions; Chang (2010) [31] also explored the causative relationship between GDP growth and carbon dioxide emissions in China using multivariate cointegration causality tests and concluded that there is a two-way causal relationship between the two variables, which is consistent with the findings of this paper.…”
Section: Spatial Autocorrelation Analysissupporting
confidence: 78%
“…From 1995 to 2007, Li and Li (2010) [40] argued that the overall carbon emissions of all regions rose annually, which is consistent with this paper's finding that year is positively connected with carbon emissions. Using the STIRPAT model and panel data methodologies, they also analysed the impact of population, economics, and technology on regional carbon emissions, concluding that rapid economic growth is the most significant factor in the increase of carbon emissions; Chang (2010) [31] also explored the causative relationship between GDP growth and carbon dioxide emissions in China using multivariate cointegration causality tests and concluded that there is a two-way causal relationship between the two variables, which is consistent with the findings of this paper.…”
Section: Spatial Autocorrelation Analysissupporting
confidence: 78%
“…This may be attributed to the positive correlation between logistics energy intensity and logistics energy consumption. Furthermore, the inefficient energy consumption structure in the logistics industry results in higher carbon emissions [53], thereby impeding LGTFP. The coefficients of economic development level and marketization degree are found to be statistically insignificant.…”
Section: Low Carbon Pilotmentioning
confidence: 99%
“…Ji et al (2022) quantified the carbon intensity (CEI) metric to capture the impact of traffic flow on emissions. Li and Sun. (2021) estimated the total carbon emissions of China's logistics industry using the IPCC's carbon accounting method.…”
Section: Carbon Trading Marketmentioning
confidence: 99%