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author:

Tao, J. (Tao, J..) [1] | Liu, M. (Liu, M..) [2] | Zhegn, Y. (Zhegn, Y..) [3] | Fu, H. (Fu, H..) [4]

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Abstract:

This study focuses on exploring the spatial and temporal evolution characteristics of the implied carbon emission efficiency of Chinese provinces and promoting the improvement of the implied carbon emission efficiency, with a view to supply reference for the low-carbon sustainable development of China. Based on the data of each province onto China in 2007, 2012 and 2017, this study applies the environmentally extended input-output model and the super-efficient SBM model to measure the implied carbon emission efficiency and investigates the spatial and temporal evolution characteristics of the implied carbon emission efficiency of Chinese provinces through kernel density estimation and ArcGIS visualization. The results show that the implied carbon emission efficiency of China's provinces and regions as a whole increased from 2007 to 2017, and that differences in implied carbon emission efficiency between provinces and regions exist and are gradually increasing. At present, China's provincial implied carbon emission efficiency is still at a low level, and the level of implied carbon emission efficiency varies significantly between regions. Overall, from 2007 to 2017, the overall carbon emission efficiency in China has been considerably improved. © The Institution of Engineering & Technology 2023.

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  • [ 1 ] [Tao J.]Xiamen University of Technology, Xiamen, 361024, China
  • [ 2 ] [Tao J.]Key Lab of Urban Environment and Health, Institute of Urban Environment, Chinese Academy of Sciences, Xiamen, 361021, China
  • [ 3 ] [Liu M.]Fuzhou University, Fuzhou, 350116, China
  • [ 4 ] [Liu M.]Key Lab of Urban Environment and Health, Institute of Urban Environment, Chinese Academy of Sciences, Xiamen, 361021, China
  • [ 5 ] [Zhegn Y.]Xiamen University of Technology, Xiamen, 361024, China
  • [ 6 ] [Fu H.]Xiamen University of Technology, Xiamen, 361024, China

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ISSN: 2732-4494

Year: 2023

Issue: 8

Volume: 2023

Page: 54-58

Language: English

Cited Count:

WoS CC Cited Count: 0

SCOPUS Cited Count:

ESI Highly Cited Papers on the List: 0 Unfold All

WanFang Cited Count:

Chinese Cited Count:

30 Days PV: 1

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