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 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.

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.

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.

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.

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.

scholarly journals Factors Affecting Energy-Related Carbon Emissions in Beijing-Tianjin-Hebei Region

2017 ◽  
Vol 2017 ◽  
pp. 1-17 ◽  
Author(s):  
Jing-min Wang ◽  
Yu-fang Shi ◽  
Xue Zhao ◽  
Xue-ting Zhang
Keyword(s):  
Carbon Emissions ◽  
Carbon Emission ◽  
Energy Intensity ◽  
Industrial Structure ◽  
Energy Structure ◽  
Factors Affecting ◽  
Economic Output ◽  
Level Model ◽  
The Impact ◽  
The Relationship

Beijing-Tianjin-Hebei is a typical developed region in China. The development of economy has brought lots of carbon emissions. To explore an effective way to reduce carbon emissions, we applied the Logarithmic Mean Divisia Index (LMDI) model to find drivers behind carbon emission from 2003 to 2013. Results showed that, in Beijing, Tianjin, and Hebei, economic output was main contributor to carbon emissions. Then we utilized the decoupling model to comprehensively analyze the relationship between economic output and carbon emission. Based on the two-level model, results indicated the following: (1) Industry sector accounted for almost 80% of energy consumption in whole region. The reduced proportion of industrial GDP will directly reduce the carbon emissions. (2) The carbon factor for CO2/energy in whole region was higher than that of Beijing and Tianjin but lower than that of Hebei. The impact of energy structure on carbon emission depends largely on the proportion of coal in industry. (3) The energy intensity in whole region decreased from 0.79 in 2003 to 0.40 in 2013 (unit: tons of standard coal/ten thousand yuan), which was lower than national average. (4) The cumulative effects of industrial structure, energy structure, and energy intensity were negative, positive, and negative, respectively.

scholarly journals Research on driving factors of China’s carbon emissions based on structural decomposition

2021 ◽  
Vol 248 ◽  
pp. 02026
Author(s):  
Hua Gao ◽  
Zhoujie Huang
Keyword(s):  
Carbon Emissions ◽  
Energy Intensity ◽  
Final Demand ◽  
Driving Factors ◽  
Structure Effect ◽  
Influence Coefficient ◽  
Intensity Effect ◽  
Demand Structure ◽  
Demand Effect ◽  
Main Factor

After further processing the input-output tables of 2007, 2012 and 2017, the carbon emissions are decomposed into four driving factors: energy intensity effect, Leontief technology effect, final demand structure effect and final total demand effect through IO-SDA model. The results show that the energy intensity effect has a significant negative effect, which is the main factor to promote the reduction of carbon emissions. The Leontief technical effect and the final total demand effect are positive effects, and the total final demand effect is the main factor leading to the increase in carbon emissions, and the effect of the final demand structure effect is not significant. In addition, the results of the influence coefficient and the inductance coefficient show that: metal smelting and rolling manufacturing, petroleum processing and coking and nuclear fuel processing, coal mining and processing, and oil and gas mining and processing industries are high-energy-consuming industries, but the status of the basic industry makes it possible to formulate energy-saving policies only in terms of technological progress.

CHINA'S VIEWS ON THE SYSTEM OF INTERNATIONAL RELATIONS IN THE XXI CENTURY: USA AND RUSSIA IN PRC FOREIGN POLICY

2021 ◽  
Vol 1 (4) ◽  
pp. 166-176
Author(s):  
D. V. GORDIENKO ◽  
Keyword(s):  
International Relations ◽  
The United States ◽  
New Approach ◽  
Republic Of China ◽  
The Usa ◽  
The Common ◽  
Community Concept ◽  
Common Destiny ◽  
The Military ◽  
The Impact

China's views on the system of international relations in the XXI century. are undergoing significant changes. The PRC is close to entering the stage of transformation into a superpower. The concept of a Community of Shared Destiny for Humanity describes China's “new” approach to international relations, which replaces the outdated model. The purpose of this article is to assess the possible consequences of the implementation of the Community Concept of the common destiny of mankind for the system of international relations in the XNUMXst century. and analysis of the national security of the People's Republic of China, the United States of America and the Russian Federation. As a result, an approach to such an assessment and consideration of changes in the policy of the states of the Russia-China-USA "strategic triangle" is proposed, which allows identifying the policy priorities of China, the USA and Russia in various regions of the world. The results of assessing the impact of the possible consequences of the implementation of the Concept of the Community of the common destiny of mankind on the system of international relations in the XXI century. can be used to substantiate recommendations to the military-political leadership of our country. It is concluded that China's views on the system of international relations in the XXI century. are of great importance for the implementation of the current economic and military policy of this country.

The Ordinary Conception of Race in the United States and Its Relation to Racial Attitudes: A New Approach

2009 ◽  
Vol 9 (1-2) ◽  
pp. 15-38 ◽  
Author(s):  
Joshua Glasgow ◽  
Julie Shulman ◽  
Enrique Covarrubias
Keyword(s):  
Racial Attitudes ◽  
The United States ◽  
Internet Survey ◽  
Racial Classification ◽  
New Approach ◽  
Biological Phenotype ◽  
The Usa ◽  
Social Race ◽  
The One ◽  
The Relationship

AbstractMany hold that ordinary race-thinking in the USA is committed to the 'one-drop rule', that race is ordinarily represented in terms of essences, and that race is ordinarily represented as a biological (phenotype- and/or ancestry-based, non-social) kind. This study investigated the extent to which ordinary race-thinking subscribes to these commitments. It also investigated the relationship between different conceptions of race and racial attitudes. Participants included 449 USA adults who completed an Internet survey. Unlike previous research, conceptions of race were assessed using concrete vignettes. Results indicate widespread rejection of the one-drop rule, as well as the use of a complex combination of ancestral, phenotypic, and social (and, therefore, non-essentialist) criteria for racial classification. No relationship was found between racial attitudes and essentialism, the one-drop rule, or social race-thinking; however, ancestry-based and phenotype-based classification criteria were associated with racial attitudes. These results suggest a complicated relationship between conceptions of race and racial attitudes.

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