The rapid advancement of artificial intelligence(AI)has significantly increased the computational load on data centers.AI-related computational activities consume considerable electricity and result in substantial car...The rapid advancement of artificial intelligence(AI)has significantly increased the computational load on data centers.AI-related computational activities consume considerable electricity and result in substantial carbon emissions.To mitigate these emissions,future data centers should be strategically planned and operated to fully utilize renewable energy resources while meeting growing computational demands.This paper aims to investigate how much carbon emission reduction can be achieved by using a carbonoriented demand response to guide the optimal planning and operation of data centers.A carbon-oriented data center planning model is proposed that considers the carbon-oriented demand response of the AI load.In the planning model,future operation simulations comprehensively coordinate the temporal‒spatial flexibility of computational loads and the quality of service(QoS).An empirical study based on the proposed models is conducted on real-world data from China.The results from the empirical analysis show that newly constructed data centers are recommended to be built in Gansu Province,Ningxia Hui Autonomous Region,Sichuan Province,Inner Mongolia Autonomous Region,and Qinghai Province,accounting for 57%of the total national increase in server capacity.33%of the computational load from Eastern China should be transferred to the West,which could reduce the overall load carbon emissions by 26%.展开更多
There are many uncertainties in the estimation of forest carbon sequestration in China, especially in Liaoning Province where various forest inventory data have not been fully utilized. By using forest inventory data,...There are many uncertainties in the estimation of forest carbon sequestration in China, especially in Liaoning Province where various forest inventory data have not been fully utilized. By using forest inventory data, we estimated forest vegetation carbon stock of Liaoning Province between 1993 and 2005. Results showed that forest biomass carbon stock increased from 68.91 Tg C in 1993 to 97.51 Tg C in 2005, whereas mean carbon density increased from 18.48 Mg·ha^-1 C to 22.33 Mg·ha^-1 C. The carbon storage of young- and middle-aged forests increased by 22.1 Tg C and 5.95 Tg C, but that of mature forests has decreased by 0.25 Tg C. The carbon stock and density of forests in Liaon- ing Province varied greatly in space： larger carbon storage and higher carbon density were primarily found in the east area. The spatial distribution of carbon density was determined by many factors, of which human activities played an important role. The forests in Liaoning Province played a positive role as a sink of atmospheric carbon dioxide. The carbon fixation ability of forests in this area was primarily derived from forest plantation and the total forest carbon sequestration can be enhanced by expanding young- and middle-aged forests.展开更多
基金supported by the Scientific&Technical Project of the State Grid(5700--202490228A--1--1-ZN).
文摘The rapid advancement of artificial intelligence(AI)has significantly increased the computational load on data centers.AI-related computational activities consume considerable electricity and result in substantial carbon emissions.To mitigate these emissions,future data centers should be strategically planned and operated to fully utilize renewable energy resources while meeting growing computational demands.This paper aims to investigate how much carbon emission reduction can be achieved by using a carbonoriented demand response to guide the optimal planning and operation of data centers.A carbon-oriented data center planning model is proposed that considers the carbon-oriented demand response of the AI load.In the planning model,future operation simulations comprehensively coordinate the temporal‒spatial flexibility of computational loads and the quality of service(QoS).An empirical study based on the proposed models is conducted on real-world data from China.The results from the empirical analysis show that newly constructed data centers are recommended to be built in Gansu Province,Ningxia Hui Autonomous Region,Sichuan Province,Inner Mongolia Autonomous Region,and Qinghai Province,accounting for 57%of the total national increase in server capacity.33%of the computational load from Eastern China should be transferred to the West,which could reduce the overall load carbon emissions by 26%.
基金supported by Fujian Provincial Science and Technology Project (2010H0020)Environmental Public-benefit Project (201009055) by providing financial assistance
文摘There are many uncertainties in the estimation of forest carbon sequestration in China, especially in Liaoning Province where various forest inventory data have not been fully utilized. By using forest inventory data, we estimated forest vegetation carbon stock of Liaoning Province between 1993 and 2005. Results showed that forest biomass carbon stock increased from 68.91 Tg C in 1993 to 97.51 Tg C in 2005, whereas mean carbon density increased from 18.48 Mg·ha^-1 C to 22.33 Mg·ha^-1 C. The carbon storage of young- and middle-aged forests increased by 22.1 Tg C and 5.95 Tg C, but that of mature forests has decreased by 0.25 Tg C. The carbon stock and density of forests in Liaon- ing Province varied greatly in space： larger carbon storage and higher carbon density were primarily found in the east area. The spatial distribution of carbon density was determined by many factors, of which human activities played an important role. The forests in Liaoning Province played a positive role as a sink of atmospheric carbon dioxide. The carbon fixation ability of forests in this area was primarily derived from forest plantation and the total forest carbon sequestration can be enhanced by expanding young- and middle-aged forests.
