Using LMDI Method to Analyze the Energy-Related CO2 Emissions in Guangdong Province, China

2013 ◽  
Vol 291-294 ◽  
pp. 1556-1561
Author(s):  
Ping Wang ◽  
Wan Shui Wu ◽  
Bang Zhu Zhu
Keyword(s):  
Co2 Emissions ◽  
Energy Intensity ◽  
Negative Impact ◽  
Scale Effects ◽  
Energy Structure ◽  
Per Capita Gdp ◽  
Lmdi Method ◽  
Greenhouse Gas Inventories ◽  
Per Capita ◽  
Population Scale

In recent years, Guangdong has achieved remarkable performance in economic development; meanwhile it is being faced with problems of increasing CO2 emissions. Following the IPCC Guidelines for National Greenhouse Gas Inventories, we estimated the energy-related CO2 emissions in Guangdong during the period of 1980-2010. We employed the logarithmic mean divisia index (LMDI) method to decompose the CO2 emissions into energy intensity, energy structure, per capita GDP and population scale effects. Besides, we deduced the calculation methods for the year by year effects, the accumulated effects and the contribution degrees. Using 1980 as the base year, the empirical results show that the accumulated effects of energy intensity and energy structure in 2010 are negative, while those of per capita GDP and population scale are positive. Per capita GDP is the chief positive influence on the CO2 emissions. Energy intensity is becoming more significant; however, its direction is instability. Population scale has a significant positive effect on the CO2 emissions. Energy structure has a negligible negative impact on the CO2 emissions. Some suggestions on CO2 emissions reduction in Guangdong are given based on the analysis.

scholarly journals The Regional Inequity of CO2 Emissions per Capita in China

2017 ◽  
Vol 9 (7) ◽  
pp. 228 ◽  
Author(s):  
Ting Liu ◽  
Wenqing Pan
Keyword(s):  
Co2 Emissions ◽  
Emission Reduction ◽  
Co2 Emission ◽  
Energy Intensity ◽  
Industrial Structure ◽  
Theil Index ◽  
Energy Structure ◽  
Co2 Emission Reduction ◽  
Index Method ◽  
Per Capita

This paper combines Theil index method with factor decomposition technique to analyze China eight regions’ inequality of CO2 emissions per capita, and discuss energy structure, energy intensity, industrial structure, and per capita output’s impacts on inequality. This research shows that: (1) The trend of China regional carbon inequality is in the opposite direction to the per capita CO2 emission level. Namely, as the per capita CO2 emission levels rise, regional carbon inequality decreases, and vice versa. (2) Per capita output factor reduces regional carbon inequality, whereas energy structure factor and energy intensity factor increase the inequality. (3) More developed areas can reduce the carbon inequality by improving the energy structure, whereas the divergence of energy intensity in less developed areas has increased to expand the carbon inequity. Thus, when designing CO2 emission reduction targets, policy makers should consider regional differences in economic development level and energy efficiency, and refer to the main influencing factors. At the same time, upgrading industrial structure and upgrading energy technologies should be combined to meet the targets of economic growth and CO2 emission reduction.

scholarly journals Comparison of CO2 emissions reduction efficiency of household fuel consumption in China

2019 ◽  
Vol 11 (4) ◽  
pp. 979 ◽  
Author(s):  
Bingjie Xu ◽  
Ruoyu Zhong ◽  
Yifeng Liu
Keyword(s):  
Natural Gas ◽  
Co2 Emissions ◽  
Negative Impact ◽  
National Level ◽  
Energy Structure ◽  
Kuznets Curve ◽  
Liquefied Petroleum Gas ◽  
Gas Consumption ◽  
Natural Gas Consumption ◽  
Per Capita

The purpose of this paper is to analyze the correlations among per capita gross domestic product (GDP), household fuel (natural gas and liquefied petroleum gas) consumption, and carbon dioxide (CO2) emissions through the environmental Kuznets curve (EKC) at the regional and national level in China using data from 2003 to 2015. The results validate the EKC assumption and show that per capita GDP is positively related to CO2 emissions; per capita natural gas consumption has a negative impact on CO2 emissions; however, per capita liquefied petroleum gas (LPG) consumption has a positive effect on CO2 emissions. Therefore, increasing natural gas consumption can effectively slow down the environmental degradation of China. Given rapid economic growth, changing the energy structure can improve the environment.

scholarly journals A Factor Decomposition on China’s Carbon Emission from 1997 to 2012 Based on IPAT-LMDI Model

2015 ◽  
Vol 2015 ◽  
pp. 1-14 ◽  
Author(s):  
Wei Li ◽  
Ya-Bo Shen ◽  
Hui-Xia Zhang
Keyword(s):  
Development Strategy ◽  
Carbon Dioxide Emissions ◽  
Energy Intensity ◽  
Industrial Structure ◽  
Scale Effects ◽  
Assessment System ◽  
Energy Structure ◽  
Low Carbon ◽  
Economic Output ◽  
Population Scale

We probe into the key factors that possess significant effects on China’s CO2emissions during 1997–2012 on the basis of IPAT-LMDI model. Carbon dioxide emissions are specifically decomposed into CO2emission intensity, energy structure, energy intensity, industrial structure, economic output, and population scale effects. Results indicate that the paramount driving factors that resulted in the growth of CO2emissions are economic output, population scale, and energy structure. In contrast, energy intensity and industrial structure generally play an outstanding role in reducing emissions. This paper constructs a new weight assessment system by introducing “contribution value-significant factor-effect coefficient” to replace “contribution value-contribution rate” in the previous literature. According to the most significant positive effect and the most negative effect from the conclusion, we point out the effective policies that can not only accelerate the target of “China’s carbon emissions per unit of GDP could be cut down by 40–45% by 2020, from 2005 levels,” but also have crucial significance on the low-carbon economic development strategy of China.

scholarly journals Analysis of influencing factors of Chinese provincial carbon emissions based on projection pursuit model and Markov transfer matrix

2019 ◽  
Vol 11 (3) ◽  
pp. 406-423 ◽  
Author(s):  
Lei Wen ◽  
Linlin Huang
Keyword(s):  
Carbon Emissions ◽  
Energy Intensity ◽  
Industrial Structure ◽  
Projection Pursuit ◽  
Energy Structure ◽  
Per Capita Gdp ◽  
Content Type ◽  
Factors Affecting ◽  
Optimal Projection ◽  
Per Capita

Purpose Climate change has aroused widespread concern around the world, which is one of the most complex challenges encountered by human beings. The underlying cause of climate change is the increase of carbon emissions. To reduce carbon emissions, the analysis of the factors affecting this type of emission is of practical significance. Design/methodology/approach This paper identified five factors affecting carbon emissions using the logarithmic mean Divisia index (LMDI) decomposition model (e.g. per capita carbon emissions, industrial structure, energy intensity, energy structure and per capita GDP). Besides, based on the projection pursuit method, this paper obtained the optimal projection directions of five influencing factors in 30 provinces (except for Tibet). Based on the data from 2000 to 2014, the authors predicted the optimal projection directions in the next six years under the Markov transfer matrix. Findings The results indicated that per capita GDP was the critical factor for reducing carbon emissions. The industrial structure and population intensified carbon emissions. The energy structure had seldom impacted on carbon emissions. The energy intensity obviously inhibited carbon emissions. The best optimal projection direction of each index in the next six years remained stable. Finally, this paper proposed the policy implications. Originality/value This paper provides an insight into the current state and the future changes in carbon emissions.

scholarly journals Spatio-Temporal Analysis of CO2 Emission Driving Force in Various Provinces in China Using the Extended STIRPAT-GWR Model

2021 ◽  
Author(s):  
Yulin Zhang
Keyword(s):  
Co2 Emissions ◽  
Co2 Emission ◽  
Energy Intensity ◽  
Industrial Structure ◽  
Temporal Analysis ◽  
Energy Structure ◽  
High Effect ◽  
Northern Regions ◽  
Spatio Temporal ◽  
Gwr Model

To fill the shortcomings of traditional research that ignores the driver’s own spatial characteristics and provide a theoretical support to formulate suitable emission reduction policies in different regions across China. In this pursuit, based on the panel data of provincial CO2 emission in 2007, 2012, and 2017, the present study employed the extended environmental impact assessment model (STIRPAT-GWR model) to study the effect of population, energy intensity, energy structure, urbanization and industrial structure on the CO2 emissions in 29 provinces across China. The empirical results show that the effect of drivers on the CO2 emissions exhibited significant variations among the different provinces. The effect of population in the southwest region was significantly lower than that of the central and eastern regions. Provinces with stronger energy intensity effects were concentrated in the central and western regions. The effect of energy structure in the eastern and northern regions was relatively strong, and gradually weakened towards the southeast region. The areas with high urbanization effect were concentrated in the central and the eastern regions. Furthermore, significant changes were observed in the high-effect regions of the industrial structure in 2017. The high-effect area showed a migration from the northwest and northeast regions in 2007 and 2012, respectively, to the southwest and southeast regions in 2017. Urbanization showed the strongest effect on the CO2 emissions, followed by population and energy intensity, and the weakest effect was exhibited by the energy and industrial structure. Thus, the effects of population and energy structure showed a downward trend, in contrary to the effect of urbanization on the CO2 emissions in China.

scholarly journals Economic Activity and Pollution:The case of Indonesia 1967-2013

2017 ◽  
Vol 18 (2) ◽  
pp. 239 ◽  
Author(s):  
Mohammad Rofiuddin ◽  
Tito Aditya Perdana ◽  
Nugroho SBM
Keyword(s):  
Linear Regression ◽  
Environmental Pollution ◽  
Co2 Emissions ◽  
Significant Influence ◽  
Economic Activity ◽  
Quadratic Equation ◽  
Gdp Per Capita ◽  
Per Capita Gdp ◽  
Curve Method ◽  
Per Capita

Increased economic activity accompanied with environmental pollution. The objective of the study was to analyze the effect of per capita GDP on CO2 emissions and to prove the hypothesis of the Kuznets environment curve. Method for analyzing data by using multiple linear regression with quadratic equation. The results show that GDP per capita has a positive and significant influence on CO2 emissions, as well as the square GDP per capita has a negative and significant influence on CO2 emissions, so the Kuznets Environment Curve's hypothesis can be proven.

scholarly journals Using the LMDI Method to Analyze the Change in Greenhouse Gas Emissions in Turkish Sectors

2021 ◽  
Author(s):  
Abdulkadir BEKTAŞ
Keyword(s):  
Co2 Emissions ◽  
Heat Production ◽  
Economic Activity ◽  
Energy Intensity ◽  
Decomposition Analysis ◽  
Manufacturing Industries ◽  
Lmdi Method ◽  
Gaseous Fuels ◽  
Turkish Economy ◽  
Determining Factors

In this study, CO2 emissions of the Turkish economy are decomposed for the 1998–2017 period for four sectors; agriculture, forestry and fishery, manufacturing industries and construction, public electricity and heat production, transport, and residential. The analyses are conducted for five fuel types; liquid, solid, gaseous fuels, biomass, and other fuels. In decomposition analysis, Log Mean Divisia Index (LMDI) method is used. The analysis results point out that energy intensity is one of the determining factors behind the change in CO2 emissions, aside from economic activity. The fuel mix component, especially for the manufacturing industries and construction sector, lowers CO2 emissions during the crisis periods when the economic activity declines. Mainly, it is found that changes in total industrial activity and energy intensity are the primary factors determining the changes in CO2 emissions during the study period. Among GDP sectors, manufacturing industries and construction and public electricity and heat production are the two sectors that dominate the change in CO2 emissions. Additionally, the residential and transport sectors’ contributions have gained importance during recent years. Among the manufacturing industries and construction, the non-metallic minerals sector contributes to CO2 emissions, followed by the chemicals sector.

scholarly journals Reviewing Local Revenue Capacity in Indonesia

2021 ◽  
Vol 8 (1) ◽  
pp. 1
Author(s):  
Metasari Kartika
Keyword(s):  
Agricultural Sector ◽  
Negative Impact ◽  
Fixed Effect Model ◽  
Provincial Government ◽  
Per Capita Gdp ◽  
Study Results ◽  
Significant Negative Impact ◽  
Per Capita ◽  
Trade Sector ◽  
Tax Capacity

Fiscal capacity through Local Own-Source Revenuedescribes the region's ability to explore existing sources of income in the region. Data from BPS (2019) on the level of regional independence shows 11 provinces in the low category, 15 provinces in the low category, and eight provinces in the moderate category. Until now, no province in Indonesia has been included in the high category of regional independence. The novelty of this study, trying to revisit the issue of Local Own-Source Revenue in Indonesia. The purpose of the study was to analyze the influence of per capita GDP variables, the value of the trade sector, and the value of the agricultural sector on Local Own-Source Revenuecapacity. Local Own-Source Revenue capacity is measured using the concept of tax capacity, namely Local Own-Source Revenuedivided by PDRB. The object of the study was 34 provinces in Indonesia during the period 2010-2019 (10 years). The research method uses an unbalanced regression panel with a fixed-effect model approach. The study results were that the per capita GDP had a positive and significant effect on Local Own-Source Revenue capacity. The trade sector had a positive and insignificant effect, and the agricultural sector had a significant negative impact on Local Own-Source Revenuecapacity. Therefore, the Provincial Government needs to continue to increase GDP per capita, issue regulations, and maintain regional conditions to support trade activities and approach the public to pay taxes, especially provincial taxes. The provincial government also needs to increase the downstream and industrialization of agricultural products to increase the capacity of Local Own-Source Revenue. Keywords: Local Own-Source Revenue; Tax Capacity  

scholarly journals Residential Energy-Related CO2 Emissions in China’s Less Developed Regions: A Case Study of Jiangxi

2020 ◽  
Vol 12 (5) ◽  
pp. 2000 ◽  
Author(s):  
Yong Yang ◽  
Junsong Jia ◽  
Chundi Chen
Keyword(s):  
Co2 Emissions ◽  
Energy Demand ◽  
Dominant Role ◽  
Energy Structure ◽  
Residential Energy ◽  
Rural Regions ◽  
Residential Sector ◽  
Per Capita ◽  
The Difference

The residential sector is the second-largest consumer of energy in China. However, little attention has been paid to reducing the residential CO2 emissions of China’s less developed or undeveloped regions. Taking Jiangxi as a case study, this paper thus aims at fully analyzing the difference of the residential energy-related CO2 emissions between urban and rural regions based on the Log-Mean Divisia Index (LMDI) and Tapio decoupling model. The main results are showed as follows: (1) Since 2008, residential energy-related CO2 emissions have increased rapidly in both urban and rural Jiangxi. From 2000 to 2017, the residential energy-related CO2 emissions per capita in rural regions rapidly increased and exceeded that in urban regions after 2015. Furthermore, the residential energy structures had become multiple in both urban and rural regions, but rural regions still had room to optimize its energy structure. (2) Over the study period, consumption expenditure per capita played the dominant role in increasing the residential energy-related CO2 emissions in both urban and rural regions, followed by energy demand and energy structure. Energy price had the most important effect on decreasing the urban and rural residential energy-related CO2 emissions, followed by the carbon emission coefficient. However, urbanization increased the urban residential energy-related CO2 emissions but decreased the CO2 emissions in rural regions. Population made marginal and the most stable contribution to increase the residential energy-related CO2 emissions both in urban and rural regions. (3) Overall, the decoupling status showed the weak decoupling (0.1) and expansive negative decoupling (1.21) in urban and rural regions, respectively.

scholarly journals Relationships between Causal Factors Affecting Future Carbon Dioxide Output from Thailand’s Transportation Sector under the Government’s Sustainability Policy: Expanding the SEM-VECM Model

Resources ◽  
2018 ◽  
Vol 7 (4) ◽  
pp. 81 ◽  
Author(s):  
Pruethsan Sutthichaimethee ◽  
Danupon Ariyasajjakorn
Keyword(s):  
Energy Consumption ◽  
Confidence Interval ◽  
Co2 Emissions ◽  
Direct Effect ◽  
Industrial Structure ◽  
Per Capita Gdp ◽  
Net Exports ◽  
Transportation Sector ◽  
Per Capita ◽  
Urbanization Rate

This research aims to analyze the relationships between causal factors likely to affect future CO2 emissions from the Thai transportation sector by developing the Structural Equation Modeling-Vector Autoregressive Error Correction Mechanism Model (SEM-VECM Model). This model was created to fill information gaps of older models. In addition, the model provides the unique feature of viable model application for different sectors in various contexts. The model revealed all exogenous variables that have direct and indirect influences over changes in CO2 emissions. The variables show a direct effect at a confidence interval of 99%, including per capita GDP (), labor growth (), urbanization rate factor (), industrial structure (), energy consumption (), foreign direct investment (), oil price (), and net exports (). In addition, it was found that every variable in the SEM-VECM model has an indirect effect on changes in CO2 emissions at a confidence interval of 99%. The SEM-VECM model has the ability to adjust to the equilibrium equivalent to 39%. However, it also helps to identify the degree of direct effect that each causal factor has on the others. Specifically, labor growth () had a direct effect on per capita GDP () and energy consumption () at a confidence interval of 99%, while urbanization rate () had a direct effect on per capita GDP (), labor growth (), and net exports () at a confidence interval of 99%. Furthermore, industrial structure () had a direct effect on per capita GDP () at a confidence interval of 99%, whereas energy consumption () had a direct effect on per capita GDP () at a confidence interval of 99%. Foreign direct investment () had a direct effect on per capita GDP () at a confidence interval of 99%, while oil price () had a direct effect on industrial structure (), energy consumption (), and net exports () at a confidence interval of 99%. Lastly, net exports () had a direct effect on per capita GDP () at a confidence interval of 99%. The model eliminates the problem of heteroskedasticity, multicollinearity, and autocorrelation. In addition, it was found that the model is white noise. When the SEM-VECM Model was used for 30-year forecasting (2018–2047), it projected that CO2 emissions would increase steadily by 67.04% (2047/2018) or 123.90 Mt CO2 Eq. by 2047. The performance of the SEM-VECM Model was assessed and produced a mean absolute percentage error (MAPE) of 1.21% and root mean square error (RMSE) of 1.02%. When comparing the performance value with the values of other, older models, the SEM-VECM Model was found to be more effective and useful for future research and policy planning for Thailand’s sustainability goals.

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