scholarly journals An Empirical Study of Carbon Emission Impact Factors Based on the Vector Autoregression Model

Energies ◽  
2021 ◽  
Vol 14 (22) ◽  
pp. 7797
Author(s):  
Wei Fan ◽  
Xi Luo ◽  
Jiabei Yu ◽  
Yiyang Dai
Keyword(s):  
Economic Growth ◽  
Carbon Emissions ◽  
Energy Intensity ◽  
Vector Autoregression ◽  
Linear Regression Analysis ◽  
Growth Effect ◽  
Impact Factors ◽  
Energy Structure ◽  
Annual Data ◽  
Intensity Effect

It is important to effectively reduce carbon emissions and ensure the simultaneous adjustment of economic development and environmental protection. Therefore, we used Kaya identity to screen the factors influencing carbon emissions and conducted preliminary qualitative analyses, including grey relation analysis and linear regression analysis, on important variables to establish a vector autoregression (VAR) model based on their annual data to empirically analyze the influencing factors of carbon emissions. The results showed that economic growth effect, energy intensity effect and embodied carbon in foreign trade were the key factors affecting carbon emissions, among which the economic growth effect contributed the most. Accordingly, we propose countermeasures including technological innovation to reduce energy intensity, the development of new energy sources to improve energy structure, acceleration of industrial structure transfer, and optimization of trade structure.

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.

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 Decomposition Analysis of Greenhouse Gas Emissions in Korea’s Transportation Sector

2019 ◽  
Vol 11 (7) ◽  
pp. 1986 ◽  
Author(s):  
Suyi Kim
Keyword(s):  
Economic Growth ◽  
Gross Domestic Product ◽  
Emission Factor ◽  
Energy Intensity ◽  
Ghg Emissions ◽  
Growth Effect ◽  
Transportation Mode ◽  
Intensity Effect ◽  
Transportation Sector ◽  
Mode Effect

This study analyzed the greenhouse gas (GHG) emissions from the transportation sector in Korea from 1990 to 2013 using Logarithmic Mean Divisia Index (LMDI) factor decomposition methods. We decomposed these emissions into six factors: The population effect, the economic growth effect due to changes in the gross domestic product per capita, the energy intensity effect due to changes in energy consumption per gross domestic product, the transportation mode effect, the energy mix effect, and the emission factor effect. The results show that some factors can cause an increase in GHG emissions predominantly influenced by the economic growth effect, followed by the population growth effect. By contrast, others can cause a decrease in GHG emissions, predominantly via the energy intensity effect. Even though the transportation mode effect has contributed to a reduction of GHG emissions, it remains relatively small compared to other factors. The energy mix and emission factor effects contributed to the reduction of GHG emissions in the early 2000s, however the effects have led to an increase of GHG emissions since the mid-2000s. Altogether, based on these results, this study suggests some GHG mitigation policies aimed at achieving the national target for this sector.

scholarly journals Research on Energy-Related CO2 Emissions Characteristics, Decoupling Relationship and LMDI Factor Decomposition in Qinghai

2021 ◽  
Vol 9 ◽  
Author(s):  
Can Huang ◽  
Yin-Jun Zhou ◽  
Jin-Hua Cheng
Keyword(s):  
Economic Growth ◽  
Growth Rate ◽  
Carbon Emissions ◽  
Average Annual Growth Rate ◽  
Utilization Efficiency ◽  
Carbon Accounting ◽  
Annual Growth ◽  
Energy Structure ◽  
Annual Growth Rate ◽  
Intensity Effect

Based on the statistical data from 1997 to 2017, with the utilization of the IPCC carbon accounting method, Tapio decoupling model, and Logarithmic Mean Divisia Index (LMDI), the temporal evolution characteristics of Qinghai’s energy-related carbon emissions, the decoupling relationship, and its driving factors were analyzed. The results indicated that 1) The carbon emissions of Qinghai showed a trend of first slowly increasing, then rapidly increasing, and finally fluctuating and decreasing. It increased from 3.85 million tons in 1997 to 14.33 million tons in 2017, with an average annual growth rate of 6.79%. The carbon emission intensity revealed a steady downward trend, from 189.82 tons/million CNY in 1997 to 54.6 tons/million CNY in 2017, with an average annual growth rate of –6.04%. 2) The relationship between carbon emissions and economic growth was represented by four types: weak decoupling, strong decoupling, expansion negative decoupling, and expansion coupling. Among them, a strong decoupling was achieved only in the five periods of 1997–1998, 1999–2000, 2001–2002, 2013–2015, and 2016–2017. 3) The structural effect of energy consumption was the paramount factor in restraining carbon emissions, followed by the energy intensity effect, while economic growth, and population size were important factors facilitating the increase in carbon emissions. To this end, Qinghai should continuously optimize its energy structure and improve energy utilization efficiency, thus achieving economic green and high-quality development.

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.

Driving Factors of Carbon Emissions in China's Municipalities: A LMDI Approach

2021 ◽  
Author(s):  
Yuanxin Liu ◽  
Yajing Jiang ◽  
Hui Liu ◽  
Bo Li ◽  
Jia-hai Yuan
Keyword(s):  
Carbon Emissions ◽  
Emission Reduction ◽  
Industrial Structure ◽  
Driving Factors ◽  
Policy Implications ◽  
Growth Effect ◽  
Structure Effect ◽  
Energy Structure ◽  
Intensity Effect ◽  
Population Effect

Abstract China, as the world’s largest carbon dioxide emitter, is bound to assume the important responsibility of energy conservation and emission reduction. To this end, each city, led by representative municipalities directly under the Central Government, must enhance efforts in carbon emission reduction to jointly realize China’s low-carbon transition. Taking four representative municipalities, namely, Beijing, Tianjin, Shanghai, and Chongqing as the case cities, this paper establishes a decomposition analysis for the driving factors of carbon emissions by applying the LMDI method covering data from 2007 to 2017. Kaya identity is used to decompose the effects into eight driving factors: GDP effect, industrial structure effect, energy intensity effect, overall energy structure effect, population effect, urbanization effect, per capita energy consumption effect, urban and rural energy structure effect. The results show that at the municipality level, the driving factors that promote the growth of carbon emissions are the GDP growth effect and the population effect, with the former still being the most important factor in the municipalities with faster economic growth; and industrial structure effect is the most important factor that inhibits the growth of carbon emissions, followed by energy structure effect. The paper thereby puts forward policy implications for China's economic policies.

Influencing Factors and Decoupling Analysis of Carbon Emissions in China's Manufacturing Industry

2021 ◽  
Author(s):  
baoling jin ◽  
ying Han
Keyword(s):  
Carbon Emissions ◽  
Manufacturing Industry ◽  
Energy Intensity ◽  
Value Added ◽  
Carbon Intensity ◽  
Energy Structure ◽  
Investment Efficiency ◽  
Fixed Asset ◽  
Carbon Productivity ◽  
Fixed Asset Investment

Abstract The manufacturing industry directly reflects national productivity, and it is also an industry with serious carbon emissions, which has attracted wide attention. This study decomposes the influential factors on carbon emissions in China’s manufacturing industry from 1995 to 2018 into industry value added (IVA), energy consumption (E), fixed asset investment (FAI), carbon productivity (CP), energy structure (EC), energy intensity (EI), investment carbon intensity (ICI) and investment efficiency (IE) by Generalized Divisia Index Model (GDIM). The decoupling analysis is carried out to investigate the decoupling states of the manufacturing industry under the pressure of "low carbon" and "economy.” Considering the technological heterogeneity, we study the influential factors and decoupling status of the light industry and the heavy industry. The results show that: (1) Carbon emissions of the manufacturing industry present an upward trend, and the heavy industry is the main contributor. (2) Fixed asset investment (FAI), industry value added (IVA) are the driving forces of carbon emissions. Investment carbon intensity (ICI), carbon productivity (CP), investment efficiency (IE), and energy intensity (EI) have inhibitory effects. The impact of the energy consumption (E) and energy structure (EC) are fluctuating. (3) The decoupling state of the manufacturing industry has improved. Fixed asset investment (FAI), industry value added (IVA) hinder the decoupling; carbon productivity (CP), investment carbon intensity (ICI), investment efficiency (IE), and energy intensity (EI) promote the decoupling.

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 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 the Influencing Factors of Regional Carbon Emissions in the Chinese Transportation Industry

Energies ◽  
2020 ◽  
Vol 13 (5) ◽  
pp. 1100 ◽  
Author(s):  
Changzheng Zhu ◽  
Meng Wang ◽  
Yarong Yang
Keyword(s):  
Size Effect ◽  
Carbon Emissions ◽  
Energy Intensity ◽  
Eastern Coast ◽  
Southern Coast ◽  
Transportation Industry ◽  
Fourth Stage ◽  
Intensity Effect ◽  
Output Effect ◽  
Economic Output

Global warming caused by excessive emissions of CO2 and other greenhouse gases is one of the greatest challenges for mankind in the 21st century. China is the world’s largest carbon emitter and its transportation industry is one of the fastest growing sectors for carbon emissions. However, China is a vast country with different levels of carbon emissions in the transportation industry. Therefore, it is helpful for the Chinese government to formulate a reasonable policy of regional carbon emissions control by studying the factors influencing the carbon emissions of the Chinese transportation industry at the regional level. Based on data from 1997 to 2017, this paper adopts the logarithmic mean divisia index (LMDI) decomposition method to analyze the influencing degree of several major factors on the carbon emissions of transportation industry in different regions, puts forward some suggestions according to local conditions, and provides references for the carbon reduction of Chinese transportation industry. The results show that (1) in 2017, the total carbon emissions of the Chinese transportation industry were 714.58 million tons, being 5.59 times of those in 1997, with an average annual growth rate of 9.89%. Among them, the carbon emissions on the Eastern Coast were rising linearly and higher than those in other regions. The carbon emissions in the Great Northwest were always lower than those in other regions, with only 38.75 million tons in 2017. (2) Economic output effect is the most important factor to promote the carbon emissions of transportation industry in various regions. Among them, the contribution values of economic output effect to carbon emissions on the Eastern Coast, the Southern Coast and the Great Northwest continued to rise, while the contribution values of economic output effect to carbon emissions in the other five regions decreased in the fourth stage. (3) The population size effect promoted the carbon emissions of the transportation industry in various regions, but the population size effect of the Northeast had a significant inhibitory influence on the carbon emissions in the fourth stage. (4) The regional energy intensity effect in most stages inhibited carbon emissions of the transportation industry. Among them, the energy intensity effects of the North Coast and the Southern Coast in the two stages had obvious inhibitory influences on carbon emissions of the transportation industry, but the contribution values of the energy intensity effect in the Great Northwest and the Northeast were positive in the fourth stage. (5) Except for the Great Southwest, the industry-scale effects of other regions had inhibited the carbon emissions of transportation industry in all regions. (6) The influences of the carbon emissions coefficient effect on carbon emissions in different regions were not significant and their inhibitory effects were relatively small.

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