Measuring Driving Factors and Decoupling Effect
of Transportation CO&lt;sub&gt;2&lt;/sub&gt; Emissions in Low-Carbon
Regions: A Case Study from Liaoning, China

Ma, Xuefei; Chen, Shiyao; Yang, Haojin

doi:10.15244/pjoes/116604

Cited by 4 publications

(3 citation statements)

References 30 publications

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“…In existing research, the indicators used to explore the driving factors of CO 2 emissions include regional population, regional economic development level, urbanization level, industrial structure changes, transportation activity and the number of patents in specific regions, etc. (Lakshmanan and Han, 1997;Dong et al, 2018;Ma et al, 2020). However, the number of regionally held patents varies by industry, and it is not clear how they relate to CO 2 emissions from transportation (Albino et al, 2014).…”

Section: Spatial Econometric Modelmentioning

confidence: 99%

The Driving Factors of Carbon Emissions in China’s Transportation Sector: A Spatial Analysis

2021

Front. Energy Res.

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Studies on the CO2 emissions from the transportation sector in China are increasing, but their findings are inconclusive. The main reason is that the spatial correlation of CO2 emissions from the regional transportation sector has been ignored in examinations of the driving factors of CO2 emissions from this sector. In this paper, new emission factors are adopted to calculate the CO2 emission levels from the transportation sector in Chinese provinces. By fully considering the spatial correlation of regional CO2 emissions and based on a two-way Durbin model incorporating both spatial and temporal fixed effects, the driving factors of CO2 emissions from the transportation sector in China are studied. The CO2 and spatial regression results for the transportation sector in China suggest the following: 1) Most of the regions with the highest CO2 emissions from the Chinese transportation sector are located on the east coast; they have gradually expanded over time to include the central and western regions. 2) The CO2 emissions from the transportation sector are higher in South China than in North China, and the regions with higher CO2 emissions have gradually shifted from north to south. 3) Transportation activity intensity, urbanization level, technological level, industrial structure and per capita GDP greatly impact CO2 emissions from the transportation sector in each province of China. Among these factors, transportation activity intensity, urbanization level, and per capita GDP exert not only direct effects but also indirect effects, whereas technological level and industrial structure exert only direct effects.

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Section: Spatial Econometric Modelmentioning

confidence: 99%

The Driving Factors of Carbon Emissions in China’s Transportation Sector: A Spatial Analysis

2021

Front. Energy Res.

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“…The Autoregressive Distributed Lag (ARDL) method works well for relatively small samples (Solaymani 2022). The system dynamics model is a complex open-parameter structure that is used to analyze carbon dioxide emission concentrations (Heidari et al 2022); the model of LMDI (logarithmic mean Divisia index) (Ma et al 2020, Meng &Li 2020Nnadiri et al 2021;Wang et al 2020a;Wang et al 2020c) is a typical technique for examining the variables that affect carbon emissions. This model is simple to explain and widely used and is not limited by zero values or residual values; in addition, the results are easily understood (Huang &Ling 2021;Meng &Li 2020;Wang et al 2018;Zhang et al 2022).…”

Section: Introductionmentioning

confidence: 99%

“…The Tapio decoupling model widely employed to evaluate the relationship between the energy efficiency of the economy and carbon emissions (Chen et al 2020;Ma et al 2020;Wang et al 2020a;Wang et al 2020c). For the analysis of spatial correlations and clustering intensity, several researchers have used the Moran's index (Wang et al 2020b;Cao et al 2019;Yaacob et al 2020) and geographic weighted regression (GWR) calculations to examine the geographical associations between the same variable and several locations, increasing the simulation's degree of fit in comparison to the linear regression model (Kilian et al 2022;Xu et al 2021).…”

Section: Introductionmentioning

confidence: 99%

Spatiotemporal evolution trend and decoupling type identification of transport carbon emissions from economic development in China

Cui,

Zhou,

Guan

et al. 2023

Environ Sci Pollut Res

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Carbon emissions are a major concern in China, and transportation is an important part of it. In this paper, data on China's 30 provinces' transport carbon emissions from 2005 to 2020 were selected to construct a spatial autocorrelation model and identified the decoupling type, which revealed the relationship between transport carbon emissions and economic development. This study suggests a regulation strategy for provincial transport carbon emissions in China based on the contribution rates of transport carbon emission variables. According to the findings, transport carbon emissions of China indicated a slow rise from 2005 to 2020, the annual growth rate has fluctuated downward, and the use of petroleum products has been the most major source. The geographical correlation of transport carbon emissions has gradually improved, and the transport carbon emission intensity has become more significant. Differences of the transport carbon emission intensity slightly increased, that were significantly regionally correlated. There were seven forms of decoupling between yearly provincial transport carbon emissions and economic development, with weak decoupling accounting for the largest proportion, 42.89%. Decoupling was achieved in 90% of the provinces in 3 five-year periods from 2006-2020. As a consequence of factor decomposition, the energy intensity, transport intensity, and economic structure played an overall inhibitory role, while the carbon emission intensity, economic scale and population played promoting roles. Economic scale was the most important spatial influencing factor.

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Machine learning in exploration the decoupling paradigm in transport

Szaruga

Załoga²

2022

Procedia Computer Science

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Measuring Driving Factors and Decoupling Effect of Transportation CO<sub>2</sub> Emissions in Low-Carbon Regions: A Case Study from Liaoning, China

Cited by 4 publications

References 30 publications

The Driving Factors of Carbon Emissions in China’s Transportation Sector: A Spatial Analysis

The Driving Factors of Carbon Emissions in China’s Transportation Sector: A Spatial Analysis

Spatiotemporal evolution trend and decoupling type identification of transport carbon emissions from economic development in China

Machine learning in exploration the decoupling paradigm in transport

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