High Resolution Carbon Dioxide Emission Gridded Data for China Derived from Point Sources

Abstract

A high spatial resolution carbon dioxide (CO2) emission map of China is proving to be essential for China's carbon cycle research and carbon reduction strategies given the current low quality of CO2 emission data and the inconsistencies in data quality between different regions. Ten km resolution CO2 emission gridded data has been built up for China based on point emission sources and other supporting data. The predominance of emissions from industrial point sources (84% of total emissions) in China supports the use of bottom-up methodology. The resultant emission map is informative and proved to be more spatially accurate than the EDGAR data. Spatial distribution of CO2 emissions in China is highly unbalanced and has positive spatial autocorrelation. The spatial pattern is mainly influenced by key cities and key regions, i.e., the Jing-Jin-Ji region, the Yangtze River delta region, and the Pearl River delta region. The emission map indicated that the supervision of 1% of total land could enable the management of about 70% of emissions in China.

Spatiotemporal Changes of China's Carbon Emissions

“…For the industrial sector, we considered emissions from fossil fuel combustions (e.g., thermal power plants) and industrial processes (e.g., cement production, lime production, iron and steel production, glass production, and ammonia production). The emission data for the industrial sector were collected from the China High Resolution Emission Gridded Data, which is based on the First China Pollution Source Census (FCPSC) and subsequent data updates from the latest survey (J. Wang, Cai, et al, ; M. Wang & Cai, ). The FCPSC is the first nationwide survey of all types of energy consumption complied from the China Energy Statistical Yearbook.…”

“…The FCPSC data set covers more than 1.58 million officially registered industrial enterprises in China and is more comprehensive than China's national official statistics (which exclude small enterprises with annual revenue less than 5 million CNY). Since the emissions from these industrial enterprises account for over 80% of total CO 2 emissions in China (W. Chen et al, ; J. Wang, Cai, et al, ; S. Zhou et al, ), small mistakes or errors in their geographical coordinates will substantially influence the accuracy of the resulting carbon emission patterns. In order to improve the accuracy, we surveyed the administrative properties (i.e., county or district locations) of all enterprises and verified their geographical coordinates to avoid conflicts.…”

“…Here we should note that the focus of this research is not on how the totals of our bottom‐up approach are different from the top‐down method, as our purpose of introducing the bottom‐up approach is to help derive the spatial pattern of China's carbon emissions at a finer resolution. However, we have performed detailed comparisons in both totals and spatial variations of carbon emissions between our method and other data sets (J. Wang, Cai, et al, ; M. Wang & Cai, ). In general, the totals from our bottom‐up approach are slightly higher than other sources and our data set shows a better performance than other data sources in representing the spatial variations of carbon emissions.…”

“…Zhang et al, ). The latter is essential for setting regional allocations of carbon emission quotas in China and developing appropriate policies at regional and local scales (Govindaraju & Tang, ; Yu et al, ), yet it remains poorly understood due to the insufficient coverage of spatiotemporal variations in CO 2 emissions in national carbon inventories (Hao et al, ; J. Wang et al, ).…”

Regional uncertainty of GOSAT XCO<sub>2</sub> retrievals in China: Quantification and attribution

“…sources from approximately 150 million enterprises throughout the country in 2012 (Wang et al, 2014;Cai et al, 2014). 91…”