Calculation and Decomposition of China’s Carbon Emissions from Transportation Energy Consumption: Based on LMDI Method

2014 ◽  
Vol 926-930 ◽  
pp. 4411-4414
Author(s):  
Mei Ling He ◽  
Xiao Hui Wu
Keyword(s):  
Economic Growth ◽  
Energy Consumption ◽  
Carbon Emissions ◽  
Energy Intensity ◽  
Influence Factor ◽  
Energy Structure ◽  
Logarithmic Mean ◽  
Transportation Energy ◽  
Negative Effect ◽  
Positive Effect

According to the calculation method of the IPCC, the paper calculates the composition and intensity of carbon emissions from transportation energy consumption in China from 2000 to 2011. Based on logarithmic mean divisia index (LMDI) decomposition technique, changes of carbon emissions quantity are analyzed by three factors which are the transportation energy intensity, the economic growth and the transportation energy structure. The results show: (1) Transportation energy intensity was significantly decreased. Under its influence carbon emission intensity from the transportation energy was decreased, indicating that the energy efficiency was improved continuously. (2) Transport carbon emissions were in a growing trend. The greatest influence factor was the economic growth which had a positive effect and enlarged transportation carbon emissions quantity. On the other hand, the factors of the transportation energy intensity had a negative effect. Except 2011, the transportation energy structure always had a negative effect, which reduced transportation carbon emissions quantity.

Study on the Relationship between Energy Consumption, Carbon Emission and Economic Growth in China

2013 ◽  
Vol 869-870 ◽  
pp. 746-749
Author(s):  
Tian Tian Jin ◽  
Jin Suo Zhang
Keyword(s):  
Economic Growth ◽  
Energy Consumption ◽  
Carbon Emissions ◽  
Carbon Emission ◽  
Energy Structure ◽  
Ardl Model ◽  
Relationship Of ◽  
The Relationship ◽  
Reducing Energy

Abstract. Based on ARDL model, this paper discussed the relationship of energy consumption, carbon emission and economic growth.The results indicated that the key to reduce carbon emissions lies in reducing energy consumption, optimizing energy structure.

scholarly journals PENGARUH KONSUMSI ENERGI DAN REZIM TERHADAP PERTUMBUHAN EKONOMI DI INDONESIA

2019 ◽  
Vol 1 (2) ◽  
pp. 401
Author(s):  
Zakiah Husna ◽  
Idris Idris
Keyword(s):  
Economic Growth ◽  
Renewable Energy ◽  
Energy Consumption ◽  
Time Series Data ◽  
Secondary Data ◽  
Series Data ◽  
Renewable Energy Consumption ◽  
Energy Regime ◽  
Negative Effect ◽  
Positive Effect

This study aims to determine the effect of energy consumption and regime on economic growth in Indonesia. The data used is secondary data in the form of time series data from 1988-2017, with documentation and library study data collection techniques obtained from relevant institutions and agencies. the variables used are economic growth (GDP), non-renewable energy consumption, renewable energy consumption and regime, the research methods used are: (1) Multiple Regression Analysis (OLS), (2) Classical Assumption Test results of research stating that: ( 1) non-renewable energy consumption has a positive effect on economic growth in Indonesia. (2) consumption of renewable energy has a positive effect on economic growth in Indonesia. (3) the energy regime has a negative effect on economic growth in Indonesia. (4) non-renewable energy consumption, renewable energy consumption and energy regime have a significant effect on economic growth in Indonesia. so only the energy regime has a negative effect on economic growth in Indonesia.

scholarly journals Decomposition of China’s Carbon Emissions Intensity from 1995 to 2010: An Extended Kaya Identity

2013 ◽  
Vol 2013 ◽  
pp. 1-7 ◽  
Author(s):  
Wei Li ◽  
Qing-Xiang Ou
Keyword(s):  
Carbon Emissions ◽  
Emission Reduction ◽  
Energy Intensity ◽  
Industrial Structure ◽  
Energy Structure ◽  
Logarithmic Mean ◽  
Development Path ◽  
Optimal Outcomes ◽  
Emissions Intensity ◽  
Kaya Identity

This paper employs an extended Kaya identity as the scheme and utilizes the Logarithmic Mean Divisia Index (LMDI II) as the decomposition technique based on analyzing CO2emissions trends in China. Change in CO2emissions intensity is decomposed from 1995 to 2010 and includes measures of the effect of Industrial structure, energy intensity, energy structure, and carbon emission factors. Results illustrate that changes in energy intensity act to decrease carbon emissions intensity significantly and changes in industrial structure and energy structure do not act to reduce carbon emissions intensity effectively. Policy will need to significantly optimize energy structure and adjust industrial structure if China’s emission reduction targets in 2020 are to be reached. This requires a change in China’s economic development path and energy consumption path for optimal outcomes.

scholarly journals Analysis of Factors Influencing Carbon Emissions in the Energy Base, Xinjiang Autonomous Region, China

2020 ◽  
Vol 12 (3) ◽  
pp. 1089
Author(s):  
Jiancheng Qin ◽  
Hui Tao ◽  
Chinhsien Cheng ◽  
Karthikeyan Brindha ◽  
Minjin Zhan ◽  
...  
Keyword(s):  
Economic Growth ◽  
Economic Development ◽  
Carbon Emissions ◽  
Energy Intensity ◽  
Economic Structure ◽  
Energy Structure ◽  
Intensity Effect ◽  
Entire Energy ◽  
Key Factor ◽  
Main Factors

Analyzing the driving factors of regional carbon emissions is important for achieving emissions reduction. Based on the Kaya identity and Logarithmic Mean Divisia Index method, we analyzed the effect of population, economic development, energy intensity, renewable energy penetration, and coefficient on carbon emissions during 1990–2016. Afterwards, we analyzed the contribution rate of sectors’ energy intensity effect and sectors’ economic structure effect to the entire energy intensity. The results showed that the influencing factors have different effects on carbon emissions under different stages. During 1990–2000, economic development and population were the main factors contributing to the increase in carbon emissions, and energy intensity was an important factor to curb the carbon emissions increase. The energy intensity of industry and the economic structure of agriculture were the main factors to promote the decline of entire energy intensity. During 2001–2010, economic growth and emission coefficient were the main drivers to escalate the carbon emissions, and energy intensity was the key factor to offset the carbon emissions growth. The economic structure of transportation, and the energy intensity of industry and service were the main factors contributing to the decline of the entire energy intensity. During 2011–2016, economic growth and energy intensity were the main drivers of enhancing carbon emissions, while the coefficient was the key factor in curbing the growth of carbon emissions. The industry’s economic structure and transportation’s energy intensity were the main factors to promote the decline of the entire energy intensity. Finally, the suggestions of emissions reductions are put forward from the aspects of improving energy efficiency, optimizing energy structure and adjusting industrial structure etc.

Regional Economic Growth and Carbon Emissions - A Case Study of Anhui Province

2013 ◽  
Vol 869-870 ◽  
pp. 1056-1062
Author(s):  
Xue Qin Wang ◽  
Cheng Xin Wang ◽  
Yun Wei Du ◽  
Jia Lu Shi
Keyword(s):  
Economic Growth ◽  
Energy Consumption ◽  
Energy Saving ◽  
Carbon Emissions ◽  
Emission Reduction ◽  
Reduction Process ◽  
Anhui Province ◽  
Energy Structure ◽  
Analysis Model ◽  
Promotion Policy

This essay tends to probe into the decoupling relationship between economic growth and carbon emissions through structuring the decoupling analysis model. The results show that: In recent years, the decoupling relationship between economic growth and carbon emissions in Anhui province has improved. Through the research about some intermediate variables, we find that the change trend of energy consumption elastic elasticity of carbon emissions and the one of GDP elastic elasticity of carbon emissions are basically the same. Meanwhile, Anhui province is relatively backward in the energy-saving and emission reduction process, carbon emissions growth and energy consumption growth did not achieve effective decoupling, which reflects that this province still has some defects in the adjustment of energy structure, energy saving and emission reduction technology promotion policy etc.

Dynamic Change and Analysis of Driving Factors of Carbon Emissions from Traffic and Transportation Energy Consumption in Jilin Province

2014 ◽  
Vol 472 ◽  
pp. 851-855 ◽  
Author(s):  
Biao Gao ◽  
Qing Tao Xu ◽  
Yu Bo Li
Keyword(s):  
Energy Consumption ◽  
Carbon Emissions ◽  
Carbon Emission ◽  
Growth Stage ◽  
Dynamic Change ◽  
Driving Factors ◽  
Jilin Province ◽  
Energy Structure ◽  
Estimation Model ◽  
Transportation Energy

Based on the traffic and transportation energy consumption, the carbon emissions of traffic and transportation energy consumption are obtained by using the estimation model of carbon emissions from 1999 to 2011 in Jilin Province, and the dynamic changes and the Environmental Kuznets Curve (EKC) of carbon emissions are analyzed. The result indicates that the carbon emission of traffic and transportation energy consumption increased continuously from 99.3750×104 t to 331.8255×104 t between 1999 and 2011 in Jilin Province, the change process is divided into three stages which include the stage of the stationary growth phase, accelerated growth stage and slow growth stage, the large consumption of diesel energy is the main reason of the rapid growth in carbon emissions. The EKC of carbon emission shows the inverted U shape roughly and the turning point appeared in 2011, after 2011, carbon emissions will decrease along with the economic growth. Based on the STIRPAT model, the study reveals that elasticity coefficients of driving factors such as population, per capita GDP, the unit GDP energy consumption, the investment of traffic and transportation, city rate, the number of private cars are 0.23440.2202-0.22470.16570.2864 and 0.2163, respectively. Jilin Province must implement effective measures to change the existing development mode of traffic and transportation, change the energy structure, and increase the innovation of scientific and technological, to strive for the realization of negative growth in carbon emissions of traffic and transportation energy consumption.

scholarly journals The Paradoxical Relationship between Renewable Energy and Economic Growth: A Cross-National Panel Study, 1990-2013

2017 ◽  
Vol 23 (2) ◽  
pp. 540-564 ◽  
Author(s):  
Ryan P Thombs
Keyword(s):  
Economic Growth ◽  
Carbon Dioxide ◽  
Renewable Energy ◽  
Energy Consumption ◽  
Carbon Emissions ◽  
Carbon Dioxide Emissions ◽  
Total Carbon ◽  
Renewable Energy Consumption ◽  
Negative Effect ◽  
Cross National

This cross-national study employs a time-series cross-sectional Prais-Winsten regression model with panel-corrected standard errors to examine the relationship between renewable energy consumption and economic growth, and its impact on total carbon dioxide emissions and carbon dioxide emissions per unit of GDP. Findings indicate that renewable energy consumption has its largest negative effect on total carbon emissions and carbon emissions per unit of GDP in low-income countries. Contrary to conventional wisdom, renewable energy has little influence on total carbon dioxide emissions or carbon dioxide emissions per unit of GDP at high levels of GDP per capita. The findings of this study indicate the presence of a “renewable energy paradox,” where economic growth becomes increasingly coupled with carbon emissions at high levels of renewable energy, and the negative effect of economic growth on carbon emissions per unit of GDP lessens as renewable energy increases. These findings suggest that public policy should be directed at deploying renewable energy in developing countries, while focusing on non-or-de-growth strategies accompanied with renewable energy in developed nations.

An Empirical Study on China's Energy Consumption, Carbon Emissions and Economic Growth

2013 ◽  
Vol 869-870 ◽  
pp. 377-380
Author(s):  
Meng Hui Liu ◽  
Kun Kun Xue
Keyword(s):  
Economic Growth ◽  
Energy Consumption ◽  
Carbon Emissions ◽  
Empirical Investigation ◽  
Var Model ◽  
Low Carbon ◽  
Low Carbon Economy ◽  
Negative Effect ◽  
The Relationship ◽  
Carbon Economy

With the development of low-carbon economy, it is necessary to explore the relationship between energy consumption, carbon emissions and the economic growth correctly. In this paper, the VAR model was proposed with analyzing the relationship between the three factories through pulse response graph. Through the empirical investigation, the result shows: increasing energy consumption can promote economic growth, while the increasing consumption will also raise emissions of carbon. However, the emissions of carbon have negative effect on economic growth. Therefore, we must correctly handle the relationship between the three factories. Thus, it offers the best way to develop the economic in this paper is to develop the low carbon economy.

scholarly journals Investigation for the Decomposition of Carbon Emissions in the USA with C-D Function and LMDI Methods

2019 ◽  
Vol 11 (2) ◽  
pp. 334 ◽  
Author(s):  
Rui Jiang ◽  
Rongrong Li ◽  
Qiuhong Wu
Keyword(s):  
Carbon Emissions ◽  
Energy Intensity ◽  
The United States ◽  
Energy Structure ◽  
Logarithmic Mean ◽  
Labor Input ◽  
Intensity Effect ◽  
New Approach ◽  
Combination Technique ◽  
The Usa

Residual problems are one of the greatest challenges in developing new decomposition techniques, especially when combined with the Cobb–Douglas (C-D) production function and the Logarithmic Mean Divisia Index (LMDI) method. Although this combination technique can quantify more effects than LMDI alone, its decomposition result has residual value. We propose a new approach that can achieve non-residual decomposition by calculating the actual values of three key parameters. To test the proposed approach, we decomposed the carbon emissions in the United States to six driving factors: the labor input effect, the investment effect, the carbon coefficient effect, the energy structure effect, the energy intensity effect, and the technology state effect. The results illustrate that the sum of these factors is equivalent to the CO2 emissions changes from t to t-1, thereby proving non-residual decomposition. Given that the proposed approach can achieve perfect decomposition, the proposed approach can be used more widely to investigate the effects of labor input, investment, and technology state on changes in energy and emission.

scholarly journals The Impact of Economic Growth, FDI and Energy Intensity on China’s Manufacturing Industry’s CO2 Emissions: An Empirical Study Based on the Fixed-Effect Panel Quantile Regression Model

Energies ◽  
2019 ◽  
Vol 12 (24) ◽  
pp. 4800 ◽  
Author(s):  
Chao-Qun Ma ◽  
Jiang-Long Liu ◽  
Yi-Shuai Ren ◽  
Yong Jiang
Keyword(s):  
Economic Growth ◽  
Co2 Emissions ◽  
Carbon Emissions ◽  
Co2 Emission ◽  
Manufacturing Industry ◽  
Energy Intensity ◽  
Quantile Regression Model ◽  
Panel Quantile Regression ◽  
The Impact ◽  
Positive Effect

Since the reform and opening-up, China’s CO2 emissions have increased dramatically, and it has become the world’s largest CO2 emission and primary energy consumption country. The manufacturing industry is one of the biggest contributors to CO2 emission, and determining the drivers of CO2 emissions are essential for effective environmental policy. China is also a vast transition economy with great regional differences. Therefore, based on the data of China’s provincial panel from 2000 to 2013 and the improved STIRPAT model, this paper studies the impact of economic growth, foreign direct investment (FDI) and energy intensity on China’s manufacturing carbon emissions through the fixed-effect panel quantile regression model. The results show that the effects of economic growth, FDI and energy intensity on carbon emissions of the manufacturing industry are different in different levels and regions, and they have apparent heterogeneity. In particular, economic growth plays a decisive role in the CO2 emissions of the manufacturing industry. Economic growth has a positive impact on the carbon emissions of the manufacturing industry; specifically, a higher impact on high carbon emission provinces. Besides, FDI has a significant positive effect on the upper emission provinces of the manufacturing industry, which proves that there is a pollution paradise hypothesis in China’s manufacturing industry, but no halo effect hypothesis. The reduction of energy intensity does not have a positive effect on the reduction of carbon emissions. The higher impact of the energy intensity of upper emission provinces on carbon emissions from their manufacturing industry, shows that there is an energy rebound effect in China’s manufacturing industry. Finally, our study confirms that China’s manufacturing industry has considerable space for emission reduction. The results also provide policy recommendations for policymakers.

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