scholarly journals Decomposition Analysis on Influence Factors of Direct Household Energy-related Carbon Emission in Guangdong Province-Based on Extended Kaya Identity

2018 ◽  
Vol 53 ◽  
pp. 04034
Author(s):  
Wenxiu Wang ◽  
Daiqing Zhao ◽  
Wenjun Wang
Keyword(s):  
Carbon Emissions ◽  
Carbon Emission ◽  
Energy Use ◽  
Effective Means ◽  
Influence Factors ◽  
Decomposition Analysis ◽  
Guangdong Province ◽  
Quantitative Model ◽  
Household Energy ◽  
Kaya Identity

The decomposition quantitative model of household energy-related carbon emission in Guangdong is established based on the extended Kaya identity with the Logarithmic Mean Divisia Index (LMDI) method. Influence factors of household energy-related carbon emission are decomposed into five factors. Main results show that total household energy-related carbon emissions in Guangdong province show increase trend from 1995 to 2016. Electric power consumption is the biggest source of household energy-related carbon emission. The results of decomposition show that population size is the first promote factor to household energy-related carbon emission in 1996-2004. Energy use level become the first promote factor in 2005-2016. Carbon emission coefficient show reduction effect, which is the first inhibit factor to energy-related carbon emission. Finally, two effective means to reduce household carbon emissions are given to Guangdong province.

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 Spatial cluster and Heterogeneity Research on Energy-Related Carbon Emissions in Districts and Counties of Guangdong Province

2019 ◽  
Vol 79 ◽  
pp. 03019
Author(s):  
Wenxiu Wang ◽  
Shangjun Ke ◽  
Daiqing Zhao ◽  
Guotian Cai
Keyword(s):  
Spatial Autocorrelation ◽  
Carbon Emissions ◽  
Carbon Emission ◽  
Guangdong Province ◽  
Total Carbon ◽  
Low Carbon ◽  
Cluster Area ◽  
Reduce Emissions ◽  
Global Spatial Autocorrelation ◽  
High Cluster

Energy-related carbon emissions in districts and counties of Guangdong province from 2005 to 2016 are researched based on spatial econometrics method in this article, and significance cluster area and heterogeneity area are precise pinpointed. Conclusions are as follows: (1) total carbon emissions and per capita carbon emissions exist significance global spatial autocorrelation in the year 2005-2016, and formed significance high-high cluster area in districts and counties of Guangzhou city, Shenzhen city and Dongguan city. It also formed three significance low-low cluster areas in districts and counties of eastern, western and northern of Guangdong province. Low-high heterogeneity area and high -low heterogeneity area often appears in the scope of high-high cluster area and low-low cluster area. (2)Carbon emission intensity not exist significance global spatial autocorrelation, but exist significance cluster area and heterogeneity area in the ecological development areas of eastern, western and northern of Guangdong province. In the end, the paper puts forward the regional and detailed policy recommendations for efficient carbon emission reduction for each cluster type region: carbon high-high cluster areas are priority reduce emissions area, heighten energy saving technology and optimize industrial structure are two grippers to reduce emissions. Low - low carbon emissions concentrated area in western of Guangdong should primarily develop high and new technology industry. Low low carbon emissions concentrated areas and high - high carbon emissions intensity concentrated area for eastern and northern of Guangdong province should try hard to wins ecological compensation at the same time focus on developing ecological tourism.

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 Revisiting Environmental Kuznets Curve in Relation to Economic Development and Energy Carbon Emission Efficiency: Evidence from Suzhou, China

Energies ◽  
2021 ◽  
Vol 15 (1) ◽  
pp. 62
Author(s):  
Ming Wen ◽  
Mingxing Li ◽  
Naila Erum ◽  
Abid Hussain ◽  
Haoyang Xie ◽  
...  
Keyword(s):  
Economic Development ◽  
Carbon Emissions ◽  
Environmental Kuznets Curve ◽  
Carbon Emission ◽  
Energy Use ◽  
Trade Openness ◽  
Positive Sign ◽  
Kuznets Curve ◽  
Total Energy Consumption ◽  
Long Run

This study empirically examines the effect of economic development on carbon emissions and revisits the environmental Kuznets curve in Suzhou, China. The study made use of the Gross Domestic Product Per Capita (GDPPC) of Suzhou, China as an indicator of economic development as it depicts the entire developmental ecosystem that indicates the level of production activities and total energy consumption. Bearing this in mind, the authors postulate that economic development directly increases carbon emissions through industrial and domestic consumptions. For this purpose, linear and non-linear approaches to cointegration are applied. The study finds the existence of an inverted U-shape relationship between economic development and carbon emission in the long run. Trade openness and industrial share are positively contributing to increasing carbon emissions. Energy use shows a positive sign but an insignificant association with carbon emissions. The study concludes that carbon emissions in Suzhou should be further decreased followed by policy recommendations.

Decomposition analysis of the decoupling process between economic growth and carbon emission in Beijing city, China: A sectoral perspective

2019 ◽  
Vol 31 (6) ◽  
pp. 961-982 ◽  
Author(s):  
Min Su ◽  
Shasha Wang ◽  
Rongrong Li ◽  
Ningning Guo
Keyword(s):  
Economic Growth ◽  
Carbon Emissions ◽  
Carbon Emission ◽  
National Level ◽  
Decomposition Analysis ◽  
Transport Sector ◽  
Beijing City ◽  
Emission Mitigation ◽  
Economic Output ◽  
Industrial Sectors

Cities play a major role in decoupling economic growth from carbon emission for their significant role in climate change mitigation from national level. This paper selects Beijing (economic center and leader of emission reduction in China) as a case to examine the decoupling process during the period 2000–2015 through a sectoral decomposition analysis. This paper proposes the decoupling of carbon emission from economic growth or sectoral output by defining the Tapio decoupling elasticity, and combined the decoupling elasticity with decomposition technique such as Logarithmic Mean Divisia Index approach. The results indicate that agriculture and industrial sectors presented strong decoupling state, and weak decoupling is detected in construction and other industrial sectors. Meanwhile, transport sector is in expansive negative decoupling while trade industry shows expansive coupling during the study period. Per-capita gross domestic product, industrial structure, and energy intensity are the most significant effects influencing the decoupling process. Agriculture and industry are conducive to decoupling of carbon emissions from economic output, while transport and trade are detrimental to the realization of strong decoupling target between 2000 and 2015. However, construction and other industrial sectors exerted relatively little minor impact on the whole decoupling process. Improving and promoting energy-saving technologies in transport sector and trade sector should be the key strategy adjustments for Beijing to reduce carbon emissions in the future. The study aims to provide effective policy adjustments for policy makers to accelerate the decoupling process in Beijing, which, furthermore, can lay a theoretical foundation for other cities to develop carbon emission mitigation polices more efficiently.

scholarly journals Association of Carbon Emissions and Circular Curve in Northwestern China

2019 ◽  
Vol 11 (4) ◽  
pp. 1156 ◽  
Author(s):  
Yaping Dong ◽  
Jinliang Xu ◽  
Menghui Li ◽  
Xingli Jia ◽  
Chao Sun
Keyword(s):  
Fuel Consumption ◽  
Carbon Emissions ◽  
Carbon Emission ◽  
Research Work ◽  
Field Tests ◽  
Quantitative Model ◽  
Northwestern China ◽  
Low Carbon ◽  
Emission Data ◽  
Circular Curve

Carbon emissions, produced by automobile fuel consumption, are termed as the key reason leading to global warming. The highway circular curve constitutes a major factor impacting vehicle carbon emissions. It is deemed quite essential to investigate the association existing between circular curve and carbon emissions. On the basis of the IPCC carbon emission conversion methodology, the current research work put forward a carbon emission conversion methodology suitable for China’s diesel status. There are 99 groups’ test data of diesel trucks during the trip, which were attained on 23 circular curves in northwestern China. The test road type was key arterial roads having a design speed greater than or equal to 60 km/h, besides having no roundabouts and crossings. Carbon emission data were generated with the use of carbon emission conversion methodologies and fuel consumption data from field tests. As the results suggested, carbon emissions decline with the increase in the radius of circular curve. A carbon emission quantitative model was established with the radius and length of circular curve, coupled with the initial velocity as the key impacting factors. In comparison with carbon emissions under circular curve section and flat section scenarios, the minimum curve radius impacting carbon emissions is 500 m. This research work provided herein a tool for the quantification of carbon emissions and a reference for a low-carbon highway design.

scholarly journals Decomposition Analysis in Decoupling Transport Output from Carbon Emissions in Guangdong Province, China

Energies ◽  
2016 ◽  
Vol 9 (4) ◽  
pp. 295 ◽  
Author(s):  
Yalan Zhao ◽  
Yaoqiu Kuang ◽  
Ningsheng Huang
Keyword(s):  
Carbon Emissions ◽  
Decomposition Analysis ◽  
Guangdong Province
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