| Along with the global temperatures rise,controlling the total amount of carbon emissions has become a consensus.Carbon emissions According to the actual situation of each region,it is necessary to study and formulate a carbon emission reduction plan that is in line with the local reality and their spatial distribution in lower level areas.The calculation of carbon emissions is mostly based on the statistical data of energy terminal consumption.The statistical data of energy terminal consumption are generally based on national or provincial level,and it is difficult to obtain on municipal or even smaller levels.According to the statistical data of energy terminal consumption,the calculation of carbon emissions at the county level and below is almost relatively few.On the basis of domestic and foreign related research,this study constructs different spatial levels of carbon emission spatialization models based on DMSP/OLS night light data(stable light data),land use data and energy terminal consumption carbon emission data,and uses relevant data to test them.Firstly,simulate the quantitative relationship model between the nighttime lighting data and the carbon emissions calculated by the energy terminal consumption statistics of 30 provinces,autonomous regions,and municipalities directly under the Central Government(except the Tibet Autonomous Region,the Hong Kong Special Administrative Region,the Macao Special Administrative Region,and Taiwan).Establish a spatialized model of carbon emissions,spatialize the carbon emissions on the grid of 1km × 1km in Henan Province(without considering carbon emissions from special point sources,such as thermal power plants,etc.),and verify the accuracy of carbon emissions calculated from provincial energy terminal consumption statistics;Then,based on land use data and different land use types of carbon emission calculation methods calculate carbon emissions from energy consumption,combined with GDP spatial distribution data,establish a carbon emission spatialization model based on land use data,and the carbon emissions of Henan Province are spatialized on a grid of 1km × 1km;Finally,the spatial data of carbon emissions based on two methods are compared and analyzed at municipal,county,and township levels to verify the simulation results of the two methods.The main research contents and conclusions are as follows:(1)The carbon emission spatialization model based on the carbon emissions calculated from the stable nighttime light data values and the statistical data of energy consumption is significantly correlated at the level of 0.01,and the goodness of fit R2 is 0.7231.According to the model,the carbon emission calculated based on the statistical data of energy terminal consumption and the carbon emission simulated according to the model are verified.At the provincial level,the root mean square error RMSE is 1651.84×104 tons,and the average relative error MRE is 7.41%,and the average relative error in Henan Province is 9.93%(within 10%).Generally speaking,the error is small and the accuracy is acceptable.Energy carbon emissions based on stable nighttime light data simulations are accurate and can be used to simulate carbon emissions.Based on the model,the carbon emission spatialization on the 1km×1km grid in Henan Province is simulated,and the carbon emissions of the grid were calculated and calculated,and the carbon emission of Henan Province from 1999 to 2013 is calculated,compared with the carbon emissions calculated from the terminal consumption statistics,the average absolute error is 9.73% and the accuracy is acceptable.(2)According to the land use type data of Henan Province and the calculation formula of carbon emissions of construction land(rural residential areas,urban construction land and other construction land),the total amount of carbon emissions is obtained,and based on the spatial distribution data of GDP,the carbon emission spatialization model based on land use data is established.According to the model,the carbon emissions of Henan Province in 2000,2005,2010 and 2013 were spatialized on a 1km × 1km grid,and the carbon emissions of the whole province were summarized.The carbon emissions calculated from the land use type data and the carbon emissions calculated from the energy terminal consumption statistics are compared.The results show that the energy carbon emissions simulated by the land use data are consistent with the calculated values of the statistical data.(3)On the municipal,county,and township levels of carbon emissions in Henan Province in 2000,2005,2010,and 2013 based on stable nighttime lighting data and land use data.Comparative analysis shows that the average relative error of the four-year comprehensive analysis of data is 11.50% at the municipal level through relative error analysis and regression analysis at the level of 0.005,and linear regression fitting shows that the goodness of fit R2 is 0.946,the significance level of the regression analysis is above 0.005,indicating that the results of the two methods are similar,so the two methods can be used to simulate carbon emissions at the municipal level;Using the same method to compare and analyze the simulation results at the county level,the four-year average relative error is 13.75%,the significant level of regression analysis is 0.005,and the goodness of fit R2 is 0.8922.It shows that that the results of the two methods are not significantly different at the county level.The difference in the city-level level has increased;Regression analysis at the township level shows that the effect of regression analysis at the township level is general.(4)The simulation results of the two methods gradually increase with the increase of the regional level.Generally speaking,the spatialization results of carbon emissions based on land use type simulations are better than those based on stable nighttime lighting data.According to the analysis,stable nighttime lighting data mainly reflects nighttime economic and social activities,and cannot fully reflect regional carbon emissions,and cannot distinguish the carbon emissions from point sources and non-point sources.The simulation results of land use data consider the spatial distribution of GDP,and the data accuracy is relatively high,which can reflect regional carbon emissions relatively accurately.(5)The simulation results of the two methods show that the accuracy of the two methods decreases gradually at the municipal,county and township levels.The municipal-level simulation accuracy is the highest and the township level is the lowest.In the case of low precision,the above two methods can be used to simulate the spatialization of carbon emissions at the municipal and county levels;In the case that both data are available,it is recommended to use land use data for carbon emission Spatialization. |
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