China's regional carbon emission intensity decomposition system

2014 ◽  
Vol 37 (5/6) ◽  
pp. 319
Author(s):  
Wei Xu Liu ◽  
Qian Wang ◽  
Qiao Mei Liang ◽  
Yi Ming Wei ◽  
Lu Tao Zhao
Keyword(s):  
Emission Intensity ◽  
Carbon Emission ◽  
Carbon Emission Intensity ◽  
Decomposition System

scholarly journals The Changes of Carbon Emission in China’s Industrial Sectors from 2002 to 2010: A Structural Decomposition Analysis and Input-Output Subsystem

2014 ◽  
Vol 2014 ◽  
pp. 1-9 ◽  
Author(s):  
Guoxing Zhang ◽  
Mingxing Liu
Keyword(s):  
Carbon Emissions ◽  
Emission Intensity ◽  
Carbon Emission ◽  
Decomposition Analysis ◽  
Final Demand ◽  
Good Effect ◽  
Input Output ◽  
Carbon Emission Intensity ◽  
Industrial Sectors ◽  
Three Components

Based on 2002–2010 comparable price input-output tables, this paper first calculates the carbon emissions of China’s industrial sectors with three components by input-output subsystems; next, we decompose the three components into effect of carbon emission intensity, effect of social technology, and effect of final demand separately by structure decomposition analysis; at last, we analyze the contribution of every effect to the total emissions by sectors, thus finding the key sectors and key factors which induce the changes of carbon emissions in China’s industrial sectors. Our results show that in the latest 8 years five departments have gotten the greatest increase in the changes of carbon emissions compare with other departments and the effect of final demand is the key factor leading to the increase of industrial total carbon emissions. The decomposed effects show a decrease in carbon emission due to the changes of carbon emission intensity between 2002 and 2010 compensated by an increase in carbon emissions caused by the rise in final demand of industrial sectors. And social technological changes on the reduction of carbon emissions did not play a very good effect and need further improvement.

scholarly journals Relationship Study on Land Use Spatial Distribution Structure and Energy-Related Carbon Emission Intensity in Different Land Use Types of Guangdong, China, 1996–2008

2013 ◽  
Vol 2013 ◽  
pp. 1-15 ◽  
Author(s):  
Yi Huang ◽  
Bin Xia ◽  
Lei Yang
Keyword(s):  
Land Use ◽  
Spatial Distribution ◽  
Emission Intensity ◽  
Carbon Emission ◽  
Agricultural Land ◽  
Industrial Land ◽  
Carbon Emission Intensity ◽  
Land Use Types ◽  
Distribution Structure ◽  
Concentration Degree

This study attempts to discuss the relationship between land use spatial distribution structure and energy-related carbon emission intensity in Guangdong during 1996–2008. We quantized the spatial distribution structure of five land use types including agricultural land, industrial land, residential and commercial land, traffic land, and other land through applying spatial Lorenz curve and Gini coefficient. Then the corresponding energy-related carbon emissions in each type of land were calculated in the study period. Through building the reasonable regression models, we found that the concentration degree of industrial land is negatively correlated with carbon emission intensity in the long term, whereas the concentration degree is positively correlated with carbon emission intensity in agricultural land, residential and commercial land, traffic land, and other land. The results also indicate that land use spatial distribution structure affects carbon emission intensity more intensively than energy efficiency and production efficiency do. These conclusions provide valuable reference to develop comprehensive policies for energy conservation and carbon emission reduction in a new perspective.

scholarly journals Crop Production and Agricultural Carbon Emissions: Relationship Diagnosis and Decomposition Analysis

2021 ◽  
Vol 18 (15) ◽  
pp. 8219
Author(s):  
Jianli Sui ◽  
Wenqiang Lv
Keyword(s):  
Economic Growth ◽  
Agricultural Economic ◽  
Carbon Emissions ◽  
Emission Intensity ◽  
Crop Production ◽  
Carbon Emission ◽  
Decomposition Analysis ◽  
Lmdi Method ◽  
Carbon Emission Intensity ◽  
Decoupling Analysis

Modern agriculture contributes significantly to greenhouse gas emissions, and agriculture has become the second biggest source of carbon emissions in China. In this context, it is necessary for China to study the nexus of agricultural economic growth and carbon emissions. Taking Jilin province as an example, this paper applied the environmental Kuznets curve (EKC) hypothesis and a decoupling analysis to examine the relationship between crop production and agricultural carbon emissions during 2000–2018, and it further provided a decomposition analysis of the changes in agricultural carbon emissions using the log mean Divisia index (LMDI) method. The results were as follows: (1) Based on the results of CO2 EKC estimation, an N-shaped EKC was found; in particular, the upward trend in agricultural carbon emissions has not changed recently. (2) According to the results of the decoupling analysis, expansive coupling occurred for 9 years, which was followed by weak decoupling for 5 years, and strong decoupling and strong coupling occurred for 2 years each. There was no stable evolutionary path from coupling to decoupling, and this has remained true recently. (3) We used the LMDI method to decompose the driving factors of agricultural carbon emissions into four factors: the agricultural carbon emission intensity effect, structure effect, economic effect, and labor force effect. From a policymaking perspective, we integrated the results of both the EKC and the decoupling analysis and conducted a detailed decomposition analysis, focusing on several key time points. Agricultural economic growth was found to have played a significant role on many occasions in the increase in agricultural carbon emissions, while agricultural carbon emission intensity was important to the decline in agricultural carbon emissions. Specifically, the four factors’ driving direction in the context of agricultural carbon emissions was not stable. We also found that the change in agricultural carbon emissions was affected more by economic policy than by environmental policy. Finally, we put forward policy suggestions for low-carbon agricultural development in Jilin province.

scholarly journals A Study on The Driving Factors and Spatial Spillover of Carbon Emission Intensity in The Yangtze River Economic Belt under Double Control Action

2019 ◽  
Vol 16 (22) ◽  
pp. 4452 ◽  
Author(s):  
Xuhui Ding ◽  
Zhongyao Cai ◽  
Qianqian Xiao ◽  
Suhui Gao
Keyword(s):  
Technological Innovation ◽  
Emission Intensity ◽  
Emission Reduction ◽  
Yangtze River ◽  
Carbon Emission ◽  
Spillover Effects ◽  
The Yangtze River ◽  
Low Carbon ◽  
Spatial Spillover ◽  
Carbon Emission Intensity

It is greatly important to promote low-carbon green transformations in China, for implementing the emission reduction commitments and global climate governance. However, understanding the spatial spillover effects of carbon emissions will help the government achieve this goal. This paper selects the carbon-emission intensity panel data of 11 provinces in the Yangtze River Economic Belt from 2004 to 2016. Then, this paper uses the Global Moran’s I to explore the spatial distribution characteristics and spatial correlation of carbon emission intensity. Furthermore, this paper constructs a spatial econometric model to empirically test the driving path and spillover effects of relevant factors. The results show that there is a significant positive correlation with the provincial carbon intensity in the Yangtze River Economic Belt, but this trend is weakening. The provinces of Jiangsu, Zhejiang, and Shanghai are High–High agglomerations, while the provinces of Yunnan and Guizhou are Low–Low agglomerations. Economic development, technological innovation, and foreign direct investion (FDI) have positive effects on the reduction of carbon emissions, while industrialization has a negative effect on it. There is also a significant positive spatial spillover effect of the industrialization level and technological innovation level. The spatial spillover effects of FDI and economic development on carbon emission intensity fail to pass a significance test. Therefore, it is necessary to promote cross-regional low-carbon development, accelerate the R&D of energy-saving and emission-reduction technologies, actively enhance the transformation and upgrade industrial structures, and optimize the opening up of the region and the patterns of industrial transfer.

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