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A Study On Energy Consumption Prediction And Energy Structure Optimization Of Beijing Under The Constraint Of Low-carbon Economy

Posted on:2019-09-21Degree:MasterType:Thesis
Country:ChinaCandidate:Y ZhangFull Text:PDF
GTID:2382330593450186Subject:Applied Economics
Abstract/Summary:PDF Full Text Request
At present,the city has consumed 3/4 of the world's energy,and also produced more than 80%of the CO2 emissions.Therefore,it is particularly important to study the relationship between energy saving and CO2 emission reduction from the perspective of city.This paper takes the city Beijing as the research object,using scenario analysis to simulate the future energy demand of cities under different scenarios.We use LEAP model to analyze the trend of energy demand and energy structure of Beijing during the year of 2017-2035 under 5 scenarios,and they are benchmarks,different economic growth rates,different industrial structures,energy conservation and synthesis.On this basis,the energy consumption structure of Beijing can be optimized by multi-objective optimization model with the target of the lowest carbon emissions of unit GDP,in order to achieve the carbon emission target.The results show that:First,the minimum and maximum energy consumption in 2035 is 85 million 100thousand TCE?tons of standard coal?and 108 million 690 thousand TCE,and the total energy demand in Beijing will reach 90 million 950 thousand TCE,which is 5.54%lower than the benchmark scenario and 1.58%lower than the low economic growth scenario,and 8.24%lower than the high proportion of second industry scenario.Under the energy saving scenario,the GDP energy intensity is only 0.156 tons of standard coal/10000 yuan,which is 43.2%lower than that of the base year,we can see that the reduction effect is obvious.Second,from the perspective of energy varieties,the consumption of natural gas increased continuously under the benchmark scenario,increasing by 3.3%annually.After 2018,it will become the first major energy consumption variety of Beijing.The power consumption will be increasing,but the growth rate is slower than natural gas,and it is expected to exceed the oil products in 2035 and become the second major energy consumption variety.The coal product declined by 6.8%in the year of 2025,and in 2025 it will increase rapidly because of the rapid increase of new energy.And oil consumption will stay as before,no longer increases or decreases.In 2035,natural gas,electricity and the clean energy will occupy an absolute leading position?68%?.Third,according to the energy consumption branch,the trend of energy consumption in the four major sectors under the benchmark scenario are as follows:the energy consumption in the third industry is increasing,the growth trend is obvious;the second industry is decreasing continuously because of the optimization of the industrial structure,and the consumption is lower than the living consumption in 2022;the consumption amount is gradually increasing,but the increase is slightly less than the third industry;the energy consumption of the primary industry is basically the same,shows a slight downward trend,and the consumption ratio is very small.Fourth,the results of multi-objective optimization model show that,after the optimization of energy structure,energy consumption can be effectively reduced and CO2 emissions can be controlled.In 2035,after the optimization of energy structure,energy consumption is 89 million 400 thousand tons of coal and 9169 tons of carbon dioxide emissions;while the non structural adjustment of the benchmark scenario,95million 990 thousand tons of coal will be consumed,and at least 9800 tons of carbon dioxide were discharged according to the proportion.After the optimization of energy structure,the GDP energy intensity is also decreasing.In 2035,it dropped to 0.1812,which is 9.6%lower than the 0.2005 of the energy consumption structure adjustment.These findings can provide a reference for future energy arrangement of Beijing.
Keywords/Search Tags:Energy consumption, Energy saving and emission reduction, Scenario analysis, LEAP model, Multiobjective optimization
PDF Full Text Request
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