A multi-region multi-sector decomposition and attribution analysis of aggregate carbon intensity in China from 2000 to 2015
Nan Liu,
Zujun Ma,
Jidong Kang and
Bin Su
Energy Policy, 2019, vol. 129, issue C, 410-421
Abstract:
Index Decomposition Analysis (IDA) has been used to analyze driving forces of aggregate carbon intensity (ACI). However, previous studies mostly explained changes in ACI from either sectoral or regional perspective, but rarely looked into driving forces of ACI from both sectoral and regional perspectives under a unified and consistent framework. To fill the research gap, this study proposed a multi-region multi-sector decomposition and attribution approach to analyze driving forces of ACI from both sectoral and regional perspectives. The empirical results in China show that the ACI declined by 33% from 2000 to 2015. From the sectoral perspective, the decline can be mainly attributed to the significant energy efficiency improvement in six high energy-intensive industries. However, the rapid growth in electricity consumption, especially in chemicals and iron and steel, and the expansion of iron and steel industry during 2000–2005 greatly offset the negative effect from energy efficiency. From the regional perspective, the ACI decline can be primarily due to an extensive improvement in energy efficiency in most regions. On the contrary, the increasing dependency on coal, particularly in Shandong, Inner Mongolia, and Guangdong, and the increased share of carbon-intensive industries in some provinces such as Guangxi and Sichuan, markedly increased ACI. Based on the results, the effectiveness of underlying sectoral and regional level policies and measures implemented for ACI reduction in China were evaluated.
Keywords: ACI; China; Multi-region; Multi-sector; LMDI; Attribution analysis (search for similar items in EconPapers)
Date: 2019
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Citations: View citations in EconPapers (16)
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Persistent link: https://EconPapers.repec.org/RePEc:eee:enepol:v:129:y:2019:i:c:p:410-421
DOI: 10.1016/j.enpol.2019.02.015
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