Energy Consumption and Structural Reformation in Chinese Northeast Old Industrial Base

2013 ◽  
Vol 448-453 ◽  
pp. 4281-4284 ◽  
Author(s):  
Shao Bo Liu
Keyword(s):  
Energy Consumption ◽  
Carbon Emissions ◽  
Carbon Emission ◽  
Industrial Structure ◽  
Energy Utilization ◽  
Energy Structure ◽  
Energy Technology ◽  
Industrial Base ◽  
Old Industrial Base ◽  
Emissions Intensity

Using IPCC methodology, the carbon emissions of Chinese Northeast Old Industrial Base is calculated, and the energy's synthesized impact on carbon emissions intensity is presented. The resulting shows that the carbon emissions in the three northeast provinces decreased 52.87% from 2000 to 2010, of which, Liaoning, Jilin and Heilongjiang are individually 60.09%, 45.47% and 54.14% lower. The implications are that the energy structure is one of the main factors in carbon emission in the Old Industrial Base of Northeast China, and its industrial structure is changing greatly due to energy consumption carbon emission. To adjust optimally the energy and industrial structure, and to develop the energy technology to promote energy utilization are recommended.

Carbon Emission of Mining Industry’s Energy Consumption in Heilongjiang Province

2015 ◽  
Vol 1092-1093 ◽  
pp. 1597-1600
Author(s):  
Zhong Hua Wang ◽  
Xin Ye Chen
Keyword(s):  
Energy Consumption ◽  
Carbon Emissions ◽  
Carbon Emission ◽  
Industrial Structure ◽  
Mining Industry ◽  
Calculation Model ◽  
Energy Utilization ◽  
Utilization Efficiency ◽  
Heilongjiang Province ◽  
Low Carbon

The need to reduce carbon emission in Heilongjiang Province of China is urgent challenge facing sustainable development. This paper aims to make explicit the problem-solving of carbon emission to find low carbon emission ways. According to domestic and foreign literatures on estimating and calculating carbon emissions and by integrating calculation methods of carbon emissions, it was not possible to consider all of the many contributions to carbon emissions. Calculation model of carbon emissions suitable to this paper is selected. The carbon emissions of energy consumption in mining industry are estimated and calculated from 2005 to 2012, and the characteristics of carbon emission are analyzed at the provincial level. It makes the point that carbon emissions of energy consumption in mining industry can be reduced when we attempt to alter energy consumption structure, adjust industrial structure and improve energy utilization efficiency.

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 Analysis of total carbon emission of the logistics industry in Guangdong and solutions to emission reduction

2021 ◽  
Vol 245 ◽  
pp. 01020
Author(s):  
Aixia Xu ◽  
Xiaoyong Yang
Keyword(s):  
Energy Consumption ◽  
Carbon Emissions ◽  
Carbon Emission ◽  
Energy Utilization ◽  
Total Carbon ◽  
Utilization Rate ◽  
Low Carbon ◽  
Logistics Industry ◽  
Direct Energy ◽  
Reduce Emissions

The input-output method is employed in this study to measure the total carbon emission of the logistics industry in Guangdong. The findings revealed that the carbon emission of direct energy consumption of the logistics industry in Guangdong is far above the actual carbon emissions, the second and third industries play a significant role in carbon emission of indirect energy consumption in the logistics industry in Guangdong. To reduce energy consumption and carbon emissions in Guangdong, it is not only important to control the carbon emissions in the logistics industry, but strengthen carbon emission detection in relevant industries, improve the energy utilization rate and reduce emissions in other industries, and move towards low-carbon sustainable development.

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 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.

Research on the Regional Low-Carbon Industrial Development: A Case of Tianjin Binhai New Area

2011 ◽  
Vol 219-220 ◽  
pp. 250-253 ◽  
Author(s):  
Xiao Ying Cui ◽  
Hui Ming Li ◽  
Lei Wang
Keyword(s):  
Energy Efficiency ◽  
Energy Consumption ◽  
Industrial Development ◽  
Carbon Emission ◽  
Large Scale ◽  
Industrial Structure ◽  
Industrial Sector ◽  
Energy Structure ◽  
Low Carbon ◽  
Tianjin Binhai New Area

Energy consumption induced by industry sector is the main source of carbon emission. So it is important to the policy making that research on the low-carbon industrial development, which is aiming to establish an industry system with low-carbon character. There are four restrictive factors on low-carbon industrial development in Tianjin Binhai New Area of China: the rather large scale of the secondary industry, the heavy industrial structure which strongly caused the increasing energy consumption, the high-carbon energy structure of industrial sector, and the lower industrial energy efficiency which has certain gap compared with other regions. Several countermeasures are proposed to reduce carbon emission induced by industrial sector, such as improving energy efficiency, optimizing energy structure, establishing multiple-access financing mechanism to encourage R&D on low-carbon technology, enhancing the development of low carbon industry, participating in the international cooperation actively, and making strategic plan of low-carbon industrial development.

scholarly journals Decomposition Analysis of Carbon Emissions from Energy Consumption in Beijing-Tianjin-Hebei, China: A Weighted-Combination Model Based on Logarithmic Mean Divisia Index and Shapley Value

2018 ◽  
Vol 10 (7) ◽  
pp. 2535 ◽  
Author(s):  
Yi Liang ◽  
Dongxiao Niu ◽  
Weiwei Zhou ◽  
Yingying Fan
Keyword(s):  
Economic Development ◽  
Energy Consumption ◽  
Carbon Emissions ◽  
Emission Reduction ◽  
Carbon Emission ◽  
Industrial Structure ◽  
Logarithmic Mean ◽  
Consumption Structure ◽  
Consumption Intensity ◽  
Weighted Combination

The Beijing-Tianjin-Hebei (B-T-H) region, who captures the national strategic highland in China, has drawn a great deal of attention due to the fog and haze condition and other environmental problems. Further, the high carbon emissions generated by energy consumption has restricted its further coordinated development seriously. In order to accurately analyze the potential influencing factors that contribute to the growth of energy consumption carbon emissions in the B-T-H region, this paper uses the carbon emission coefficient method to measure the carbon emissions of energy consumption in the B-T-H region, using a weighted combination based on Logarithmic Mean Divisia Index (LMDI) and Shapley Value (SV). The effects affecting carbon emissions during 2001–2013 caused from five aspects, including energy consumption structure, energy consumption intensity, industrial structure, economic development and population size, are quantitatively analyzed. The results indicated that: (1) The carbon emissions had shown a sustained growth trend in the B-T-H region on the whole, while the growth rates varied in the three areas. In detail, Hebei Province got the first place in carbon emissions growth, followed by Tianjin and Beijing; (2) economic development was the main driving force for the carbon emissions growth of energy consumption in B-T-H region. Energy consumption structure, population size and industrial structure promoted carbon emissions growth as well, but their effects weakened in turn and were less obvious than that of economic development; (3) energy consumption intensity had played a significant inhibitory role on the carbon emissions growth; (4) it was of great significance to ease the carbon emission-reduction pressure of the B-T-H region from the four aspects of upgrading industrial structure adjustment, making technological progress, optimizing the energy structure and building long-term carbon-emission-reduction mechanisms, so as to promote the coordinated low-carbon development.

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 Impact and Acting Path of Carbon Emission Trading on Carbon Emission Intensity of Construction Land: Evidence from Pilot Areas in China

2020 ◽  
Vol 12 (19) ◽  
pp. 7843
Author(s):  
Lu Li ◽  
Jie Dong ◽  
Yan Song
Keyword(s):  
Energy Consumption ◽  
Carbon Emissions ◽  
Emission Intensity ◽  
Carbon Emission ◽  
Industrial Structure ◽  
Emission Trading ◽  
Carbon Trading ◽  
Construction Land ◽  
Trading Market ◽  
Carbon Emission Trading

Recently, the environmental and resource crisis caused by excessive energy consumption has aroused great concern worldwide. China is a major country of energy consumption and carbon emissions, and has attempted to build a carbon emission trading market to reduce carbon emissions. This practice helps to promote the carbon trading projects for both regional carbon emission reduction and sustainable development in the pilot areas, as well as having important theoretical and practical significance for the further improvement of carbon emission trading policies. In this study, we first used the difference-in-difference (DID) model to evaluate the impact of carbon emission trading on the carbon emission intensity of construction land (CEICL). The results showed that the carbon emission trading policy can significantly reduce CEICL in the pilot areas. Furthermore, we adopted the quantile regression model to explore the mechanism and acting path of carbon emission trading on CEICL. The results show that the increase in carbon trading volume (CTV) can effectively reduce the CEICL. However, a high carbon trading price (CTP) tends to reduce the suppressing effect of carbon emission trading on CEICL. Additionally, carbon emission trading also affects CEICL through the indirect acting paths of industrial structure and energy intensity. Finally, we propose to promote regional low-carbon development from the perspective of developing a carbon emission trading market nationwide, rationalizing the carbon quota and trading price mechanism, optimizing the regional industrial structure, and improving the energy consumption structure.

Measure of Carbon Emissions in Jiangsu Province and Analyses of Factors

2011 ◽  
Vol 71-78 ◽  
pp. 2262-2265
Author(s):  
Jian Hua ◽  
Jun Ren
Keyword(s):  
Carbon Dioxide ◽  
Carbon Emissions ◽  
Carbon Dioxide Emissions ◽  
Industrial Structure ◽  
Jiangsu Province ◽  
Energy Structure ◽  
High Carbon ◽  
The Status ◽  
Per Capita ◽  
Emissions Intensity

We calculate the carbon dioxide emissions from the combustion of energy and production process of cement in Jiangsu Province from 1990 to 2009.Through the indicators such as carbon emissions intensity, per capita carbon emissions, we analyze the status and trends of carbon dioxide emissions in Jiangsu Province. Based on the factors of industrial structure, energy structure and high-carbon products, we give some suggestions.

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