Abstract

Electricity is an essential factor of production. The trade of goods and services between different industrial sectors in different regions may lead to the transfer of embodied carbon emissions in the power sector, which makes the problem of “carbon leakage” widespread. As the world’s largest CO2 emitter, China’s systematic identification of the patterns and structural characteristics of embodied carbon transfer in its power sector is essential for reasonably defining emission reduction responsibilities and scientifically formulating emission reduction policies. Few studies have focused on interprovincial transfer characteristics of embodied carbon emissions in the power sector. Based on the multi-regional input-output tables of China in 2010, 2012, 2015, and 2017, this paper combined the complex network theory to construct the interprovincial power embodied carbon transfer networks (PECTNs1) of China, analyzed the topological nature, community structure and identified the key provinces in the networks with the help of complex network indicators. The results indicate that both direct and embodied carbon emissions from China’s power sector present an increasing trend during the study period. The scale and scope of power carbon emission transfer between provinces have been expanding, showing an transfer trend from west to east and north to south. China’s PECTNs have small-world characteristics. From 2010 to 2017, the sub-regional phenomenon in China’s PECTNs gradually decreased. In the network community, the provinces with stable relationships are close to each other and have closer carbon correlation. Coal resource-based and large economic provinces with high power demand tend to be the core of the networks. The position of Xinjiang in the networks is gradually prominent. According to the results of the study, differentiated carbon emission reduction schemes are formulated based on the roles played by different provinces to provide ideas for embodied carbon reduction among provinces in China’s power sector.

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