Accuracy analysis of inverting provincial-level carbon emissions from night-time light data in China: comparison based on international carbon emission data. Issue 1 (November 2020)

• Record Type: Journal Article
• Authors: Yang, Xu; Liu, Xianzhao
• Is Part Of: IOP conference series. Volume 601:Issue 1(2020)
• Publication Date: 2020-11
• Subjects: Earth sciences -- Periodicals; Environmental sciences -- Congresses; Environmental sciences -- Periodicals; 550.5
• DOI: 10.1088/1755-1315/601/1/012046
• Languages: English
• ISSNs: 1755-1307

Abstract

Abstract: It is an indisputable fact that carbon emissions lead to global warming. Finding a rapid and accurate method for estimating carbon emissions is the prerequisite for making real-time emission reduction measures. In this paper, an estimation method for quick inversion of provincial-level carbon emissions in China is proposed by using night-time light data. This method was based on the corrected night-time light image and combined with the statistical data of the built-up area to extract the total night light value (TDN) in the built-up areas of 30 provinces (Municipalities directly under the Central Government and autonomous regions were collectively referred to as provinces; Tibet, Hong Kong, Macao and Taiwan were not involved here) in Chinese mainland from 1997 to 2017. The regression equation was established by using the TDN of the built-up areas in each province from 1997 to 2014 and the provincial-level carbon emission data released by CEADs (China emission accounts and datasets) in the same period, and then the TDN values from 2015 to 2017 were used as the independent variable to estimate the carbon emission of each province according to the established regression equation. Finally, we used the entropy method and carbon emission allocation model to distribute China's national-level carbon emission data released by the international authoritative databases to each province and compared them with the provincial-level carbon emissions estimated by the above regression equations from 2015 to 2017. The results show that: (1) There was a significant linear relationship between the established carbon emission estimation models in all provinces, with R2 values greater than 0.8 except Beijing, Hainan and Shanxi. (2) Comparing the difference between the estimated carbon emissions and the carbon emissions allocated to provinces by the database, except for Shandong, Shanxi, Inner Mongolia and Shaanxi provinces, the errors of the other provinces were relatively small, RMSE and MAE were less than 260mt, and the MAPE of most provinces were less than 50%, indicating that the estimation models have high goodness-of-fit and accuracy. (3)The provincial-level carbon emissions allocated by the four international databases from 2015 to 2017 and the carbon emissions estimated by the model were plotted separately, and it is found that the corresponding scatter points of most provinces were distributed near the 1:1 line, which once again showed that the carbon emissions inverted based on night-time light data were close to the carbon emissions allocated to the provinces by each database, especially the provincial-level carbon emissions from CEADs database. The above results demonstrate that this method can provide a faster and more accurate estimation of provincial-level carbon emissions for China.