Wang, M. and Cai, B. (2017) A two-level comparison of CO2 emission data in China: evidence from three gridded data sources. Journal of Cleaner Production, 148, pp. 194-201. (doi: 10.1016/j.jclepro.2017.02.003)
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Abstract
Carbon dioxide (CO2) emissions from fossil-fuel combustion are a global concern, and since 2007, China has become the nation with the highest amount of CO2 emissions. In order to understand China's carbon science research and reduction policies, it is crucial to benchmark CO2 emission estimates from different sources at multiple administrative levels. Therefore, this paper firstly compares the Emission Database for Global Atmospheric Research (EDGAR), the Fossil Fuel Data Assimilation System (FFDAS), and the China High Resolution Emission Gridded Data (CHRED) against energy census data from the National Bureau of Statistics of China (NBSC) at the province level. Results revealed that CHRED—today's finest resolution and most bottom-up approach of CO2 emission estimation in China—is the most consistent dataset with NBSC among the three (with both Pearson's R and Kendall's τ of 0.86). Secondly, EDGAR and FFDAS are compared with CHRED at the prefectural level. Finally, empirical models were developed to reconcile EDGAR and FFDAS with CHRED at both province and prefectural levels to facilitate future longitudinal studies of CO2 emissions in China.
Item Type: | Articles |
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Additional Information: | The research was funded by the project entitled An Emission-Transport-Exposure Model Based Study on the Evaluation of the Environmental Impact of Carbon Market (No. 71673107) supported by the National Natural Science Foundation of China. |
Status: | Published |
Refereed: | Yes |
Glasgow Author(s) Enlighten ID: | Wang, Dr Mingshu |
Authors: | Wang, M., and Cai, B. |
College/School: | College of Science and Engineering > School of Geographical and Earth Sciences |
Journal Name: | Journal of Cleaner Production |
Publisher: | Elsevier |
ISSN: | 0959-6526 |
ISSN (Online): | 1879-1786 |
Published Online: | 04 February 2017 |
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