scholarly journals Examining the Driving Factors of the Direct Carbon Emissions of Households in the Ebinur Lake Basin Using the Extended STIRPAT Model

2021 ◽  
Vol 13 (3) ◽  
pp. 1339
Author(s):  
Ziyuan Chai ◽  
Zibibula Simayi ◽  
Zhihan Yang ◽  
Shengtian Yang
Keyword(s):  
Energy Consumption ◽  
Carbon Emissions ◽  
Carbon Emission ◽  
Driving Factors ◽  
Series Data ◽  
Lake Basin ◽  
Stirpat Model ◽  
Consumption Structure ◽  
Ebinur Lake ◽  
Ebinur Lake Basin

In order to achieve the carbon emission reduction targets in Xinjiang, it has become a necessary condition to study the carbon emission of households in small and medium-sized cities in Xinjiang. This paper studies the direct carbon emissions of households (DCEH) in the Ebinur Lake Basin, and based on the extended STIRPAT model, using the 1987–2017 annual time series data of the Ebinur Lake Basin in Xinjiang to analyze the driving factors. The results indicate that DCEH in the Ebinur Lake Basin during the 31 years from 1987 to 2017 has generally increased and the energy structure of DCEH has undergone tremendous changes. The proportion of coal continues to decline, while the proportion of natural gas, gasoline and diesel is growing rapidly. The main positive driving factors affecting its carbon emissions are urbanization, vehicle ownership and GDP per capita, while the secondary driving factor is residents’ year-end savings. Population, carbon intensity and energy consumption structure have negative effects on carbon emissions, of which energy consumption structure is the main factor. In addition, there is an environmental Kuznets curve between DCEH and economic development, but it has not yet reached the inflection point.

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.

Analysis of Impacting Factors of Regional Carbon Emissions Based on the STIRPAT Mode - A Case Study of Shaanxi Province

2014 ◽  
Vol 962-965 ◽  
pp. 1431-1436
Author(s):  
Wei Zhang ◽  
Jin Suo Zhang
Keyword(s):  
Economic Growth ◽  
Energy Consumption ◽  
Carbon Emissions ◽  
Industrial Structure ◽  
Shaanxi Province ◽  
Stirpat Model ◽  
Consumption Structure ◽  
Carbon Emissions Reduction ◽  
Impacting Factors

Studying on the regional carbon emissions impacting factor and its effect will contribute greatly to formulation sound regional carbon emissions reduction policy. As a main province of energy development in the western of China, Shaanxi province is facing growing pressure to reduce carbon emissions. In this paper, carbon emissions impacting factors of Shaanxi were explored from aspects of population,economic growth, urbanization, industrial structure, technological progress and energy consumption structure by STIRPAT model and ridge regression method,then the contribution rate of impacting factors to carbon emissions increment were calculated. The results shows that population, economic growth, urbanization and energy consumption have positive impacting on the growth of carbon emissions in Shaanxi, among them, the economic growth is the decisive factor that pulling carbon emissions growth the economic growth. Industrial structure and technology progress had adverse effect on carbon emissions in Shaanxi, in comparison, the effect of optimizing of industrial structure to inhibit the carbon emissions in Shaanxi is greater than the effect of adjustment of energy consumption structure.

scholarly journals Study on Influencing Factors of Carbon Emissions from Energy Consumption of Shandong Province of China from 1995 to 2012

2014 ◽  
Vol 2014 ◽  
pp. 1-12 ◽  
Author(s):  
Jiekun Song ◽  
Qing Song ◽  
Dong Zhang ◽  
Youyou Lu ◽  
Long Luan
Keyword(s):  
Energy Consumption ◽  
Carbon Emissions ◽  
Industrial Structure ◽  
Shandong Province ◽  
Stirpat Model ◽  
Per Capita Gdp ◽  
Consumption Structure ◽  
Per Capita ◽  
The Impact ◽  
Consumption Intensity

Carbon emissions from energy consumption of Shandong province from 1995 to 2012 are calculated. Three zero-residual decomposition models (LMDI, MRCI and Shapley value models) are introduced for decomposing carbon emissions. Based on the results, Kendall coordination coefficient method is employed for testing their compatibility, and an optimal weighted combination decomposition model is constructed for improving the objectivity of decomposition. STIRPAT model is applied to evaluate the impact of each factor on carbon emissions. The results show that, using 1995 as the base year, the cumulative effects of population, per capita GDP, energy consumption intensity, and energy consumption structure of Shandong province in 2012 are positive, while the cumulative effect of industrial structure is negative. Per capita GDP is the largest driver of the increasing carbon emissions and has a great impact on carbon emissions; energy consumption intensity is a weak driver and has certain impact on carbon emissions; population plays a weak driving role, but it has the most significant impact on carbon emissions; energy consumption structure is a weak driver of the increasing carbon emissions and has a weak impact on carbon emissions; industrial structure has played a weak inhibitory role, and its impact on carbon emissions is great.

Analysing Nexus between Economic Growth, GHG Emissions, and Energy Consumption for India

2021 ◽  
Vol 17 (1) ◽  
pp. 1-16
Author(s):  
Asim Hasan ◽  
Rahil Akhtar Usmani
Keyword(s):  
Economic Growth ◽  
Energy Consumption ◽  
Greenhouse Gas Emissions ◽  
Greenhouse Gas ◽  
Carbon Emissions ◽  
Series Data ◽  
Gas Emissions ◽  
Current Time ◽  
Causal Influence ◽  
Integration Test

Rising greenhouse gas emissions is an important issue of the current time. India’s massive greenhouse gas emissions is ranked third globally. The escalating energy demand in the country has opened the gateway for further increase in emissions. Recent studies suggest strong nexus between energy consumption, economic growth, and carbon emissions. This study has the objective to empirically test the aforementioned interdependencies. The co-integration test and multivariate vector error correction model (VECM) are used for the analysis and the Granger Causality test is used to establish the direction of causality. The time-series data for the period of 1971–2011 is used for the analysis. The results of the study confirm strong co-integration between variables. The causality results show that economic growth exerts a causal influence on carbon emissions, energy consumption exerts a causal influence on economic growth, and carbon emissions exert a causal influence on economic growth. Based on the results, the study suggests a policy that focuses on energy conservation and gradual replacement of fossil fuels with renewable energy sources, which would be beneficial for the environment and the society.

scholarly journals Digital Mapping of Soil Organic Carbon Using Sentinel Series Data: A Case Study of the Ebinur Lake Watershed in Xinjiang

2021 ◽  
Vol 13 (4) ◽  
pp. 769
Author(s):  
Xiaohang Li ◽  
Jianli Ding ◽  
Jie Liu ◽  
Xiangyu Ge ◽  
Junyong Zhang
Keyword(s):  
Remote Sensing ◽  
Organic Carbon ◽  
Soil Organic Carbon ◽  
Prediction Accuracy ◽  
Lake Basin ◽  
Arid Areas ◽  
Annual Average ◽  
Machine Learning Methods ◽  
Ebinur Lake ◽  
Ebinur Lake Basin

As an important evaluation index of soil quality, soil organic carbon (SOC) plays an important role in soil health, ecological security, soil material cycle and global climate cycle. The use of multi-source remote sensing on soil organic carbon distribution has a certain auxiliary effect on the study of soil organic carbon storage and the regional ecological cycle. However, the study on SOC distribution in Ebinur Lake Basin in arid and semi-arid regions is limited to the mapping of measured data, and the soil mapping of SOC using remote sensing data needs to be studied. Whether different machine learning methods can improve prediction accuracy in mapping process is less studied in arid areas. Based on that, combined with the proposed problems, this study selected the typical area of the Ebinur Lake Basin in the arid region as the study area, took the sentinel data as the main data source, and used the Sentinel-1A (radar data), the Sentinel-2A and the Sentinel-3A (multispectral data), combined with 16 kinds of DEM derivatives and climate data (annual average temperature MAT, annual average precipitation MAP) as analysis. The five different types of data are reconstructed by spatial data and divided into four spatial resolutions (10, 100, 300, and 500 m). Seven models are constructed and predicted by machine learning methods RF and Cubist. The results show that the prediction accuracy of RF model is better than that of Cubist model, indicating that RF model is more suitable for small areas in arid areas. Among the three data sources, Sentinel-1A has the highest SOC prediction accuracy of 0.391 at 10 m resolution under the RF model. The results of the importance of environmental variables show that the importance of Flow Accumulation is higher in the RF model and the importance of SLOP in the DEM derivative is higher in the Cubist model. In the prediction results, SOC is mainly distributed in oasis and regions with more human activities, while SOC is less distributed in other regions. This study provides a certain reference value for the prediction of small-scale soil organic carbon spatial distribution by means of remote sensing and environmental factors.

scholarly journals Mitigating the Energy Consumption and the Carbon Emission in the Building Structures by Optimization of the Construction Processes

Energies ◽  
2021 ◽  
Vol 14 (11) ◽  
pp. 3287
Author(s):  
Alireza Tabrizikahou ◽  
Piotr Nowotarski
Keyword(s):  
Life Cycle ◽  
Energy Consumption ◽  
Structural Optimization ◽  
Carbon Emissions ◽  
Carbon Emission ◽  
Construction Materials ◽  
High Energy ◽  
Building Structures ◽  
Life Cycle Stages ◽  
Reduction Of Energy Consumption

For decades, among other industries, the construction sector has accounted for high energy consumption and emissions. As the energy crisis and climate change have become a growing concern, mitigating energy usage is a significant issue. The operational and end of life phases are all included in the building life cycle stages. Although the operation stage accounts for more energy consumption with higher carbon emissions, the embodied stage occurs in a time-intensive manner. In this paper, an attempt has been made to review the existing methods, aiming to lower the consumption of energy and carbon emission in the construction buildings through optimizing the construction processes, especially with the lean construction approach. First, the energy consumption and emissions for primary construction materials and processes are introduced. It is followed by a review of the structural optimization and lean techniques that seek to improve the construction processes. Then, the influence of these methods on the reduction of energy consumption is discussed. Based on these methods, a general algorithm is proposed with the purpose of improving the construction processes’ performance. It includes structural optimization and lean and life cycle assessments, which are expected to influence the possible reduction of energy consumption and carbon emissions during the execution of construction works.

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.

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