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1

Decomposing the decoupling of carbon emissions and economic growth in China’s power industry

100%
Zhou J., Guang F., Du S.
Polish Journal of Environmental Studies
|
2017
|
tom 26
|
nr 5
The power industry is the leading source of man-made carbon emissions in China, and it is supposed to assume most of the responsibility for reducing carbon emissions. To study the decoupling status between carbon emissions and economic growth in China’s power industry, a new OECD decoupling analysis with LMDI model is employed in this paper. The results are as follows: 1. Growth and volatility are the main characteristic features of carbon emissions in the power industry, and carbon emissions increased from 25,059.65 ktce in 1995 to 100,805.75 ktce in 2014, with an annual average growth rate of 15.11%. 2. Per capita output effect, energy structure effect, and population scale effect play a positive role in the increment of carbon emissions, with contributions of 202.69%, 1.42%, and 19.96%, respectively. Energy intensity effect is the only driving force on the decline of carbon emissions, with a contribution rate of -124.07%. 3. There exists a weak decoupling relationship between carbon emissions and economic growth in the power industry for most of the study years. It should be noted that energy intensity effect plays a prominent role in the development of decoupling.
2

Carbon emissions scenario prediction of the thermal power industry in the Beijing-Tianjin-Hebei region based on a back propagation neural network optimized by an improved particle swarm optimization algorithm

100%
Zhou J., Du S., Shi J., Guang F.
Polish Journal of Environmental Studies
|
2017
|
tom 26
|
nr 4
Rapid economic growth in the Beijing-Tianjin-Hebei region has been accompanied by a dramatic increase in carbon emissions. Therefore, a precise study of forecasting carbon emissions is important as regards curbing them. To identify the influence factors of carbon emissions and effectively predict carbon emissions under the three different GDP growth rate scenarios in the Beijing-Tianjin-Hebei thermal power industry, we employed a combination of the improved particle swarm optimization-back propagation algorithm (IPSO-BP) with scenario prediction. The results are as follows: 1) The influencing degree of carbon emissions factors from strong to weak are the installed capacity of thermal power, thermal power generation, urbanization rate, GDP, and utilization ratio of units (with grey correlation degrees of 0.9262, 0.9247, 0.8683, 0.8082, and 0.7704, respectively). 2) Compared with the BP neural network, it is testified that using the IPSO-BP neural network model with an annual average relative error of 2.53%, while the prediction precision of BP neural network is 5.07%. Besides, the number of iterations to achieve the optimal solution is approximately reduced by 33%. 3) GDP is the contributor to the increment of carbon emissions of the power industry, whereby GDP growth rate can be reduced appropriately to curb carbon emissions, avoiding excessive pursuit of economic growth.
3

Scenario analysis of carbon emissions of China’s power industry based on the improved particle swarm optimization-support vector machine model

100%
Zhou J., Guang F., Tang R.
Polish Journal of Environmental Studies
|
2018
|
tom 27
|
nr 1
The power industry, as the primary source of carbon emissions across China, should take more responsibility to effectively reduce carbon emissions. Affected by various factors, carbon emissions from the power industry show non-linear and non-stationary characteristics. To forecast carbon emissions precisely and efficiently, this paper proposes an improved particle swarm optimization (IPSO)-support vector machine (SVM) model combined with scenario analysis. Grey relativity analysis (GRA) is applied to identify and construe the major influencing factors. Based on factors including economic growth, urbanization rate, total electricity consumption, net coal consumption rate, and thermal power installed capacity, 48 kinds of development scenarios are set during 2016-20. Compared with other methods, the effectiveness of IPSOSVM has been proved to have the best forecasting performance. The prediction results indicate that carbon emissions from China’s power industry will be 128691.59-149137.32kt in 2020. And the influencing level of each factor differs a lot in different development scenarios. Furthermore, there exists a certain decoupling between carbon emissions of China’s power industry and economic growth.
4

Prediction of CO2 emissions based on the analysis and classification of decoupling

100%
Zhou J., Guang F., Gao Y.
Polish Journal of Environmental Studies
|
2017
|
tom 26
|
nr 6
This in-depth paper studies the issue of energy-related CO₂ emissions of China using sample data from 1980 to 2015. Due to the lack of official data, CO₂ emissions are first calculated by the recommended IPCC method. It shows that CO₂ emissions in China present an “S” type in shape. Then the Tapio decoupling index is applied to investigate the relationship between CO₂ emissions and economic growth. This suggests that weak decoupling is the main state during the study period and the decoupling trend is M-shaped. Moreover, the study years are divided into decoupling years and re-link years according to the decoupling relationship, and the ReliefF algorithm is proposed to verify the feasibility of the classification and judge the influencing weights of different driving factors. The ascending order is: actual GDP, urbanization rate, industrial structure, population, energy structure, and electricity consumption. Finally, a hybrid model of grey neural network model (GNNM) based on grey model (GM) and BP neural network (BPNN) is established to forecast CO₂ emissions. This demonstrates that the GNNM model has a better capacity for forecasting CO₂ emissions and capturing the non-linear and non-stationary characteristics of CO₂ emissions.
5

Analyzing and predicting CO2 emissions in China based on the LMDI and GA-SVM model

100%
Zhou J., Yu X., Guang F., Li W.
Polish Journal of Environmental Studies
|
2018
|
tom 27
|
nr 2
With the effect of CO₂ emissions being the primary cause of the greenhouse effect, a selection and analysis study of driving factors of CO₂ emissions is vital to controlling growth from the source. This paper decomposes CO₂ emissions based on the logarithmic mean division index (LMDI) from three industries and residential consumption in China during the period 2000-14. A genetic algorithm-support vector machine (GA-SVM) was established. The eight driving factors as input variables have been innovated to apply the forecasting model. In the case study, the data set of driving factors from 2000 to 2009 is selected as training samples, and the other data set of driving factors from 2010 to 2014 is regarded as test samples. The results show that the factor decomposed based on the LMDI method of CO₂ emissions is very rational and can greatly improve forecast accuracy. The effectiveness of the GA-SVM model has been proven by the final simulation, which indicates that the proposed model outperforms a back propagation neural network (BPNN) model and a single SVM model in forecasting CO₂ emissions.
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