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Energies 2018, 11(9), 2398; https://doi.org/10.3390/en11092398

Influencing Factors and Scenario Forecasts of Carbon Emissions of the Chinese Power Industry: Based on a Generalized Divisia Index Model and Monte Carlo Simulation

1
School of Statistics, Dongbei University of Finance and Economics, Dalian 116025, China
2
School of Public Administration, Dongbei University of Finance and Economics, Dalian 116025, China
3
Editorial Department, Dongbei University of Finance and Economics, Dalian 116025, China
4
School of Economics, Qingdao University, Qingdao 266071, China
*
Author to whom correspondence should be addressed.
Received: 18 August 2018 / Revised: 6 September 2018 / Accepted: 8 September 2018 / Published: 11 September 2018
(This article belongs to the Special Issue Modeling and Simulation of Carbon Emission Related Issues)
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Abstract

The power industry is the industry with the most direct uses of fossil fuels in China and is one of China’s main carbon industries. A comprehensive and accurate analysis of the impacts of carbon emissions by the power industry can reveal the potential for carbon emissions reductions in the power industry to achieve China’s emissions reduction targets. The main contribution of this paper is the use of a Generalized Divisia Index Model for the first time to factorize the change of carbon emissions in China’s power industry from 2000 to 2015, and gives full consideration to the influence of the economy, population, and energy consumption on the carbon emissions. At the same time, the Monte Carlo method is first used to predict the carbon emissions of the power industry from 2017 to 2030 under three different scenarios. The results show that the output scale is the most important factor leading to an increase in carbon emissions in China’s power industry from 2000 to 2015, followed by the energy consumption scale and population size. Energy intensity levels have always promoted carbon emissions reduction in the power industry, where energy intensity and carbon intensity effects of energy consumption have great potential to mitigate carbon levels. By setting the main factors affecting carbon emissions in the future three scenarios, this paper predicts the carbon emissions of China’s power industry from 2017 to 2030. Under the baseline scenario, the maximum probability range of the potential annual growth rate of carbon emissions by the power industry in China from 2017 to 2030 is 1.9–2.2%. Under the low carbon scenario and technological breakthrough scenario, carbon emissions in China’s power industry continue to decline from 2017 to 2030. The maximum probability range of the potential annual drop rate are measured at 1.6–2.1% and 1.9–2.4%, respectively. The results of this study show that China’s power industry still has great potential to reduce carbon emissions. In the future, the development of carbon emissions reduction in the power industry should focus on the innovation and development of energy saving and emissions reduction technology on the premise of further optimizing the energy structure and adhering to the low-carbon road. View Full-Text
Keywords: power industry; carbon emissions; Generalized Divisia Index; scenario forecast; Monte Carlo method power industry; carbon emissions; Generalized Divisia Index; scenario forecast; Monte Carlo method
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Zhu, L.; He, L.; Shang, P.; Zhang, Y.; Ma, X. Influencing Factors and Scenario Forecasts of Carbon Emissions of the Chinese Power Industry: Based on a Generalized Divisia Index Model and Monte Carlo Simulation. Energies 2018, 11, 2398.

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