Spatio-temporal evolution characteristics of carbon emissions from road transportation in the mainland of China from 2006 to 2021

Zhang, Lanyi; Weng, Dawei; Xu, Yinuo; Hong, Baoye; Wang, Shuo; Hu, Xisheng; Zhang, Yuanyuan; Wang, Zhanyong

doi:10.1016/j.scitotenv.2024.170430

Cited by 17 publications

(11 citation statements)

References 60 publications

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“…Lanyi Zhang and Dawei Weng studied the provincial road transportation carbon emissions in China from 2006 to 2021, finding that China's road transportation carbon emissions exhibit an east-high and west-low distribution. They analyzed five factors influencing transportation carbon emissions [19]. Lijuan Su and Yatao Wang measured the spatial and temporal evolution characteristics and convergence of agricultural carbon emissions, conducted a comparative analysis of regional differences, and investigated the spatial correlation and spatial spillover effects using panel data from 31 provinces in China from 2005 to 2020 [20].…”

Section: Literature Reviewmentioning

confidence: 99%

Analysis of Factors Influencing the Spatial and Temporal Variability of Carbon Intensity in Western China

Yang,

Wang,

2024

Sustainability

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Understanding the spatio-temporal differentiation of carbon intensity factors is crucial for setting scientific and reasonable carbon emission reduction targets. This study, based on relevant data from the western regions for the years 2010–2019, analyzes the influencing factors of the spatio-temporal distribution differences in carbon intensity in these areas. Additionally, the Grey Forecasting Model was utilized to predict the development trend of average carbon intensity in the western regions. The results indicate the following: (1) The temporal dimension of carbon intensity in the western regions shows an overall declining trend with local rebounds, while the high-value areas of spatial carbon intensity are concentrated in the northern part of the study area. (2) Per capita Gross Domestic Product, energy consumption per unit of Gross Domestic Product and investment in industrial pollution control have a positive impact on carbon intensity, whereas investment in the energy industry and per capita disposable income of residents have a negative impact. (3) Energy consumption per unit of Gross Domestic Product is the factor with the highest degree of explanation in univariate analysis; interaction detection results suggest that the core factors of spatial distribution differences in carbon intensity are energy consumption and urban development. (4) Predictions using the Grey Forecasting Model for the development of carbon intensity in the western regions show a year-by-year decline, consistent with carbon intensity control targets. Based on these conclusions, this paper proposes policy recommendations focusing on improving regional economic coordination mechanisms, increasing investment in industrial pollution control, managing energy industry expenditures, adjusting the proportion of the urban population, and enhancing the per capita disposable income of residents.

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Section: Literature Reviewmentioning

confidence: 99%

Analysis of Factors Influencing the Spatial and Temporal Variability of Carbon Intensity in Western China

Yang,

Wang,

2024

Sustainability

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“…For instance, some scholars have assessed carbon emissions for specific land use types, such as croplands [9], wetlands [10,11], and forests [12]. (b) Ecological and economic compensation factors have been incorporated into the study of the spatial and temporal characteristics of carbon emissions [13]. In fact, the process and mechanism of carbon emissions from land use under the economic and social system are far more complex than those under natural ecosystems, which is reflected in the fact that the effect of land use on carbon emissions is often transmitted through other factors, and the determinants of carbon emissions are more varied and related to each other.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Notably, enhancing the intensive utilization level of construction land can contribute to carbon emissions. Some scholars have simulated the effect of land use on carbon emissions under different policy schemes from the perspective of system dynamics [22,23], and discussed the spatiotemporal coupling relationship between land use and carbon emissions from a spatial perspective [13]. With advancements in research, a few scholars have delved into the effect of specific types of construction land on carbon emissions.…”

Section: Literature Reviewmentioning

confidence: 99%

Construction Land Transfer Scale and Carbon Emission Intensity: Empirical Evidence Based on County-Level Land Transactions in Jiangsu Province, China

Li,

Wang,

Liu

et al. 2024

Land

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The expansion of the construction land scale has been vital in supporting rapid economic development and meeting social needs. However, the spatial heterogeneity in the effect of construction land scale on carbon emission intensity at the county level remains underexplored. Therefore, comprehensively investigating the relation between the construction land transfer scale and carbon emission intensity holds substantial research value. Using panel data from 2007 to 2021, this study analyzes the spatiotemporal differentiation characteristics of carbon emission intensity and the effect of construction land scale on carbon emission intensity at the county level in Jiangsu Province, China. The findings reveal that carbon emission intensity at the county level in Jiangsu Province generally exhibits a continuous downward trend over time and a spatial distribution characterized by a gradual decrease from the southern counties to the central and northern counties. Moreover, there is a significant positive relation between the construction land transfer scale and carbon emission intensity, a conclusion supported by robustness tests. Furthermore, mediating analysis indicates that reduction of the construction land transfer scale exhibits a significant promoting effect on green technology innovation and industrial structure upgrading, which, in turn, has a significant inhibitory effect on carbon emission intensity. The impact of the construction land transfer scale from different sources, supply methods, types, and of county economic strength on carbon emission intensity has significant heterogeneity.

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“…The Logarithmic Mean Divisia Index (LMDI) method, a subset of Index Decomposition Analysis (IDA), is widely used to develop carbon-emission decomposition models [8]. These models are able to accurately identify and quantify various influential factors and their drivers [11][12][13]. Numerous studies have applied the LMDI method to assess factors impacting carbon emissions in the manufacturing sector [14][15][16].…”

Section: Introductionmentioning

confidence: 99%

Differential Quantitative Analysis of Carbon Emission Efficiency of Gansu Manufacturing Industry in 2030

Tan,

Zhang,

Zhang

et al. 2024

Sustainability

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Decomposition analysis and forecasting of carbon emissions in manufacturing are crucial for setting sustainable carbon-reduction targets. Given the varied carbon-emission efficiencies across sectors, this study applied the Logarithmic Mean Divisia Index (LMDI) decomposition method to analyze the drivers of carbon emissions in Gansu’s manufacturing sector, encompassing high, medium, and low-efficiency industries, and it identified vital factors affecting carbon emissions. A localized Long-range Energy Alternatives Planning System (LEAP) model for Gansu was also developed. This model includes six developmental scenarios to project future carbon emissions. The study results are as follows: (1) LMDI decomposition indicates that increased carbon emissions in the manufacturing industry primarily result from economic growth in less efficient sectors and the dominance of moderately efficient ones. (2) Under Optimization Scenario 6, a 50.82 × 104 ton reduction in carbon emissions is projected for Gansu’s manufacturing sector by 2030 compared to 2020, marking the carbon peak. These outcomes provide valuable insights for policy reforms in Gansu’s manufacturing industry, aiming for carbon peaking by 2030.

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Spatio-temporal evolution characteristics of carbon emissions from road transportation in the mainland of China from 2006 to 2021

Cited by 17 publications

References 60 publications

Analysis of Factors Influencing the Spatial and Temporal Variability of Carbon Intensity in Western China

Analysis of Factors Influencing the Spatial and Temporal Variability of Carbon Intensity in Western China

Construction Land Transfer Scale and Carbon Emission Intensity: Empirical Evidence Based on County-Level Land Transactions in Jiangsu Province, China

Differential Quantitative Analysis of Carbon Emission Efficiency of Gansu Manufacturing Industry in 2030

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