scholarly journals Breaking down barriers on PV trade will facilitate global carbon mitigation

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Mudan Wang ◽  
Xianqiang Mao ◽  
Youkai Xing ◽  
Jianhong Lu ◽  
Peng Song ◽  
...  

AbstractThe global trade of solar photovoltaic (PV) products substantially contributes to increases in solar power generation and carbon emissions reductions. This paper depicts global PV product trade patterns, explores emissions reduction potential, and evaluates the impeding effect of tariff barriers on global PV product trade and emissions reductions. Solar power generation will result in a reduction of emissions in a range of 50–180 gigatons of carbon dioxide equivalent (GtCO2e) between 2017 and 2060 in a business as usual (BAU) scenario. Compared with BAU, during 2017–2060, global total solar cell and module production and installation will increase by roughly 750 gigawatts (GW) if half of the status quo trade barrier are removed, while it will decrease by 160–370 GW under tensioned trade barrier scenarios. Trade barrier reduction by half from the 2017 status quo level will increase the net carbon emissions mitigation potential by 4–12 GtCO2e by 2060, while extra trade barrier imposition will result in global net carbon emissions mitigation potential decreasing by up to 3–4 GtCO2e by 2060. Well-coordinated policy and institutional reforms are recommended to facilitate PV product trade and to deliver the related global environmental benefits.

2021 ◽  
Vol 11 (4) ◽  
pp. 1776
Author(s):  
Young Seo Kim ◽  
Han Young Joo ◽  
Jae Wook Kim ◽  
So Yun Jeong ◽  
Joo Hyun Moon

This study identified the meteorological variables that significantly impact the power generation of a solar power plant in Samcheonpo, Korea. To this end, multiple regression models were developed to estimate the power generation of the solar power plant with changing weather conditions. The meteorological data for the regression models were the daily data from January 2011 to December 2019. The dependent variable was the daily power generation of the solar power plant in kWh, and the independent variables were the insolation intensity during daylight hours (MJ/m2), daylight time (h), average relative humidity (%), minimum relative humidity (%), and quantity of evaporation (mm). A regression model for the entire data and 12 monthly regression models for the monthly data were constructed using R, a large data analysis software. The 12 monthly regression models estimated the solar power generation better than the entire regression model. The variables with the highest influence on solar power generation were the insolation intensity variables during daylight hours and daylight time.


2021 ◽  
Vol 1879 (3) ◽  
pp. 032070
Author(s):  
Fadhil Mahmood Oleiwi ◽  
Naseer K. Kasim ◽  
Ahmed F. Atwan

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