Research On Carbon Emission Prediction And Influencing Factors In Kunming City

Carbon dioxide emissions has become a global focus on major issues,not only has a significant influence on the global environment and ecology,also with the production,consumption and way of life of human society are closely related.In this paper,based on the grey system GM(1,1)prediction method,the theory of multiple linear regression,the STIRPAT model,BP neural network forecasting method,combination of kunming city from 2000 to 2015,carbon dioxide emissions and its influence factors to establish the corresponding prediction model,to forecast the carbon emissions from 2016 to 2020.From the fitting results of the four models,it can be seen that the prediction accuracy is high and the authenticity is reliable.The regression equations of STIRPAT model and multivariate linear regression model are simple,fast and efficient.GM(1,1)prediction model is applicable to short-term or stable prediction of growth rate(decline rate).When establishing the multiple linear regression model,the degree of influence can be ranked from high to low: per capita GDP,energy intensity,urbanization,fossil fuel consumption ratio and population.It is predicted that the total carbon dioxide emissions in kunming during the 13 th five-year plan period will be 7567.9305~ 93.1610 million tons.Based on the improved logarithmic mean declination method,the driving factors of carbon emission growth in kunming city from 2000 to 2015 were analyzed.In kunming,the following conclusions: from 2000 to 2015,carbon dioxide emissions grew by an average of 8.3%,the positive factors influencing the kunming carbon dioxide emissions growth are: per capita GDP,urbanization,population,coefficient of carbon emissions,the carbon dioxide,the contribution rate of growth was 50.98%,0.96%,10.83%,3.25 %;The negative factors include: energy intensity,rural population proportion and fossil fuel consumption ratio,which contribute to the growth of carbon dioxide by-20.06%,-9.08% and-4.84%,respectively.In the city of kunming much starker choices-and graver consequences-in planning constraints,combined with the multiple linear regression model,the STIRPAT model,BP neural network model to predict the factors impact on carbon emissions and carbon dioxide per unit of GDP index,through combining with the data published in 2016,was found that the predicted results of BP neural network model are more close to actual situation.During the following conclusion: much starker choices-and graver consequences-in,GDP growth was 7% ~ 11%,the carbon dioxide emissions on the whole,tend to decline,and with the improvement of average annual GDP growth,carbon dioxide emissions is falling;When energy intensity decreases by 10% to 20%,as energy intensity decreases,the more carbon dioxide emissions decrease.When the urbanization rate is between 73% and 76%,the carbon dioxide emissions generally show a downward trend.When fossil fuel consumption is between 76 and 80 per cent,carbon dioxide emissions rise.When the fossil fuel consumption ratio is 70 to 75 percent,co2 emissions are reduced.The carbon emission intensity per unit of GDP is the carbon emission intensity.During the 13 th five-year plan period of kunming city,the carbon emission intensity decreased.The influence of energy intensity on carbon emission intensity is small.With the increase of urbanization rate and fossil fuel consumption rate,the carbon emission intensity decreases slowly.