New provincial CO2 emission inventories in China based on apparent energy consumption data and updated emission factors

Yuli Shan, Jianghua Liu, Zhu Liu, Xinwanghao Xu, Shuai Shao, Peng Wang and Dabo Guan ()

Applied Energy, 2016, vol. 184, issue C, 742-750

Abstract: This study employs “apparent energy consumption” approach and updated emissions factors to re-calculate Chinese provincial CO2 emissions during 2000–2012 to reduce the uncertainty in Chinese CO2 emission estimates for the first time. The study presents the changing emission-socioeconomic features of each provinces as well. The results indicate that Chinese provincial aggregated CO2 emissions calculated by the apparent energy consumption and updated emissions factors are coincident with the national emissions estimated by the same approach, which are 12.69% smaller than the one calculated by the traditional approach and IPCC default emission factors. The provincial aggregated CO2 emissions increased from 3160 million tonnes in 2000 to 8583 million tonnes in 2012. During the period, Shandong province contributed most to national emissions accumulatively (with an average percentage of 10.35%), followed by Liaoning (6.69%), Hebei (6.69%) and Shanxi provinces (6.25%). Most of the CO2 emissions were from raw coal, which is primarily burned in the thermal power sector. The analyses of per capita emissions and emission intensity in 2012 indicates that provinces located in the northwest and north had higher per capita CO2 emissions and emission intensities than the central and southeast coastal regions. Understanding the emissions and emission-socioeconomic characteristics of different provinces is critical for developing mitigation strategies.

Keywords: CO2 emissions accounting; Emissions socioeconomics; Energy flows; Chinese provinces (search for similar items in EconPapers)
Date: 2016
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Citations: View citations in EconPapers (156)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:appene:v:184:y:2016:i:c:p:742-750

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DOI: 10.1016/j.apenergy.2016.03.073

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