Grey Forecasting Model for CO2 Emissions

Guo Lin Bao; Hong Qi Hui

doi:10.4028/www.scientific.net/AMR.518-523.1664

Paper Titles

The Research of Soil and Natural Colonized Plants in Lead-Zinc Tailings of Huize
p.1640

Time Lag between Sap Flow and Climatic Factors in Arid Environments
p.1647

Trace Elements of Precipitation in the Shigatse Region, Southern Tibetan Plateau
p.1652

A Comparative Study about Carbon Emissions
p.1657

Grey Forecasting Model for CO₂ Emissions
p.1664

High Precision Pb Isotope Determination by Using ²⁰⁷Pb-²⁰⁴Pb Double Spike Revisited
p.1669

Numerical Simulation of Atmospheric Diffusion of Radon Emanating From Flat Ground Uranium Tailings Impoundment in Different Heights
p.1677

Tracking Carbon Emissions from China’s Textile Sector
p.1682

Recent and Future Climate Change in Xinjiang, NW China
p.1689

HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 518-523Grey Forecasting Model for CO2 Emissions

Grey Forecasting Model for CO₂ Emissions

Abstract:

CO₂ is the most frequently implicated in global warming among the various greenhouse gases associated with climate change. Chinese government has been taking serious measures to control energy consumption to reduce CO₂ emissions. This study applies the grey forecasting model to estimate future CO₂ emissions and carbon intensity in Shijiazhuang from 2010 until 2020. Forecasts of CO2 emissions in this study show that the average residual error of the GM(1, 1) is below 1.5%. The average increasing rate of CO₂ emissions will be about 6.71%; and the carbon intensity will be 2.10 tons/10⁴GDP until year 2020. If the GDP of Shijiazhuang city can be quadruple, the carbon intensity will be half to the 2005 levels until 2020. The findings of this study provide a valuable reference with which the Shijiazhuang government can formulate measures to reduce CO² emissions by curbing the unnecessary the consumption of energy.

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Periodical:

Advanced Materials Research (Volumes 518-523)

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1664-1668

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.518-523.1664

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Online since:

May 2012

Authors:

Guo Lin Bao, Hong Qi Hui

Keywords:

Carbon Intensity, Forecasting, Grey Model

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