Present Situation And Trend Prediction Of CO₂Emissions In Hebei Province

With the rapid development of industrialization and economic globalization, largeamounts of greenhouse gases based on CO2by human activities were released into theatmosphere, which led to global climate warming and has been a serious threat to thesustainable development of human society. As the largest carbon emissions national,China has taken accordingly measures to address climate change. It is clearly presentedthat CO2emissions should reduce17%by the end of2015in China’s "Twelfth Five-YearPlan" on March,2011, this is the new challenge for carbon emission levels of each area.Hebei province is a city with heavy industry, large energy consumption as well ashuge carbon emission. An important task for Hebei Province in the new period is thathow to achieve a low-carbon path in the rapid development of the economy. In practice,predicting the future CO2emission is the research basis of carbon emission reductionwork in Hebei Province, can provide an important reference in the formulation of CO2emission control measures for decision-makers.There is no published monitoring data of CO2emissions. Firstly, we used thematerial balance method to estimate CO2emissions caused by the burning of fossil fuels,and used discharge coefficient method to estimate CO2emissions in the industrialproduction process in Hebei Province, then the calculation results were shown in theform of a list display, it can clearly understand the characteristics of CO2emission inHebei Province, at the same time, we come to the conclusion that CO2emissionreduction potential in Hebei province is the black metal smelting and rolling processingindustry, electricity, heat production and supply industry.Secondly, according to the characteristics of gray and nonlinear etc of CO2emission, we established the gray neural network model, introduced nonlinear weightparticle swarm optimization algorithm when considering that the network is easy to fallinto local optimum, and set up CO2emission prediction model of Hebei province that isIPSO-GNN model. Thirdly, using gray model, BP neural network model, gray neuralnetwork model and IPSO-GNN model respectively to verify prediction accuracy of CO2emission, the results showed that the established model in the paper is accurate, and canbe regarded as a more rational, scientific method of CO2emissions prediction in Hebeiprovince.Finally, we used IPSO-GNN model to forecast CO2emissions during2012-2010in Hebei Province. The results showed that CO2emissions will increase in Hebei Provincein the future, but the target of unit GDP CO2emissions reducing18%proposed by《Hebeiprovince “Eleventh Five Year plan” to control greenhouse gas emissions》is possible,at the same time, putting forward some specific opinions according to the predictionresults and CO2emissions characteristics in Hebei Province.