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Research On The Health Impact Of Low Carbon Development Based On China’s Provincial Level Power Sector Optimization Model

Posted on:2019-09-15Degree:DoctorType:Dissertation
Country:ChinaCandidate:J X HuiFull Text:PDF
GTID:1361330590451801Subject:Ecology
Abstract/Summary:PDF Full Text Request
As the main emission sector for carbon dioxide and air pollutants,the mitigation actions of the power sector will definitely have a significant impact on air pollution and human health.Previous researches on health impacts of carbon emissions reductions has focused on global,national,or case studies scale.However,carbon policies are usually made and applied at regional levels,resulting in rather different regional benefit to cost ratios and creating so-called winner and loser regions.To fill the research gap,this study developed the MESEIC model,a provincial-level power sector optimization model that includes 32 provinces and regions(of which Inner Mongolia is divided into east Inner Mongolia and west Inner Mongolia)and 68 inter-provincial power transmission lines.Coupled MESEIC with the air quality model and the IHIA intergrated health impacts assessment model,this research established an intergrated assessment system for the health synergy effect of mitigation policies.Based on this intergrated assessment system,we analyzed China’s short-term carbon emission reduction policies(NDC targets)and long-term 2°C temperature rise control targets,and we got national and provincial-scale power sector technology development pathways,economic costs of mitigation policies,emissions of conventional pollutants and the distribution pattern of corresponding health impact.China’s carbon emission reduction policies can avoid premature deaths of 19,962 and 26,061~126,272 people in 2030 and 2050 respectively,and the corresponding health benefits are 18~62% and 1.1~12.3 times of carbon emission reduction costs.Taking the shared social economic pathway(SSP3)which representing high mitigation and adaptation challenges as an example,in 2050,the power sector will implement carbon emission reduction to achieve the 2°C target,which will increase the proportion of renewable energy generation from 11.74% to 23.48%.Compared with non-emission reduction scenario,Inner Mongolia and Heilongjiang need to generate 920 TWh and 212 TWh of extra wind power,Tibet needs 179 TWh of extra hydropower.Shandong receives a large amount of electricity from other region.Shandong’s coal power has dropped by 796 TWh,and 15273 people have avoided premature death.The climate policy also led to the “coal power transfer”.Coal power in Shanxi and Jilin increased sharply;their policy costs were as high as 12 and 8.7 billion RMB.At the same time,the deterioration of air quality caused 6,630 and 5,925 people to die prematurely in these two provinces.In summary,this study identifies the regions that are prone to health benefits/losses and the reasons behind.For developed regions,their electricity demand has been met by other regions,thus these regions are prone to health benefits,such as Beijing,Shandong,Shanghai,Guangdong and Liaoning.Power output regions that are relatively close to developed regions,who have abundant coal resources and low technology progress rate of non-biomass renewable energy are regions that prone to health losses,such as Inner Mongolia,Shaanxi,Xinjiang,and Jilin.In order to avoid health loss and enhance public acceptance of national and provincial mitigation policies,this study proposes solutions.Guiding the control of electricity demand in developed regions(such as ensuring the decoupling of economic development from energy use)and establishing a regional benefit compensation mechanism(such as providing support for renewabl power generation)in health loss regions are really important.When the power demand decreases from 15,064 TWh to 4,822 TWh in 2050,the number of health loss regions will decrease from 4 to 0;when the annual decrease rate of renewable energy technology capital investment cost is more than 5%,China will have no health loss region in the year of 2050.
Keywords/Search Tags:climate policy, power sector, health co-benefit, optimization model
PDF Full Text Request
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