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On The Evolution Law And Forecast Of China's Terminal Energy Consumption

Posted on:2020-11-16Degree:MasterType:Thesis
Country:ChinaCandidate:L RuanFull Text:PDF
GTID:2392330578968799Subject:Management Science and Engineering
Abstract/Summary:PDF Full Text Request
Energy is essential for human production and life.With the development of economy and the expansion of industry,the demand for energy is increasing day by day,and energy crisis has appeared all over the world.As the largest energy consumer in the world,China pays special attention to the hot issues of energy consumption.The terminal energy is the part of energy that directly participates in production and consumption.Its changing characteristics have a strong effect on the formulation of national energy policy.On the other hand,adjustments to energy must understand the future energy consumption.Therefore,it is of great significance to study the changing law of China's terminal energy consumption and to forecast its consumption.At present,few scholars have studied the characteristics of terminal energy consumption change,and few have used the characteristics of change to predict.Based on this,through the analysis of the current situation of China's terminal energy consumption,this paper finds that China's terminal energy consumption is increasing year by year,showing an "S" trend.China's terminal energy consumption intensity is decreasing year by year,but there are regional differences.There is an S-shaped change path between per capita terminal energy consumption and per capita GDP.Coal is still the main source of terminal energy consumption in China,but its proportion is gradually decreasing,with more increase in electricity and other clean energy sources.From the analysis of the influence factors of terminal energy consumption,it can be concluded that GDP per capita plays a promotive role in promoting the consumption of terminal energy for production,while the intensity of energy consumption is the main restraining factor.Population and industrial structure generally have little influence on the consumption of terminal energy.The major factors affecting the end-use energy consumption are the proportion of urban population,the per capita disposable income of urban residents and the consumption level of residents.Using the social network analysis method,the overall network structure analysis shows that the terminal energy consumption of each province is closely related,and there has been a serious spillover effect.Through centrality analysis,it is concluded that the southeast region has a high degree of centrality and is more active in the network.The central region and several economically developed regions have high intermediary centers and strong control in the network.The central and southern regions are close to the center of the network,while the northwest regions are close to the edge of the network.Through block model analysis,it is found that Beijing and Tianjin had a large demand for energy in the early period,but gradually formed a large two-way spillover area in combination with the North.In the early stage,the southeast region could be satisfied by energy exchanges with each other,but later,due to the rapid economic development and excessive energy demand,it became a net overflow plate,constantly attracting the energy supply of northwest or southwest.Based on the discovery of the evolution law of terminal energy consumption,a variety of single models are used to predict.Then these single models are combined,and the combination model is designed by three methods:criterion difference method,reciprocal variance method and optimal linear method.The optimal linear method with the highest prediction accuracy is determined,and the total energy consumption of China in the period of2019-2021 is forecasted.Then the terminal energy consumption structure of China is forecasted in a similar way,and the most suitable combination model is obtained to forecast the terminal energy consumption structure of China from 2019 to 2021.
Keywords/Search Tags:Terminal Energy, Energy Evolution, Energy Forecasting, Combination Model
PDF Full Text Request
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